awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
https://github.com/jphall663/awesome-machine-learning-interpretability
Last synced: about 10 hours ago
JSON representation
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Technical Resources
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Open Source/Access Responsible AI Software Packages
- Scikit-learn - learn.org/stable/modules/decomposition.html#sparse-principal-components-analysis-sparsepca-and-minibatchsparsepca) | "a variant of [principal component analysis, PCA], with the goal of extracting the set of sparse components that best reconstruct the data.” |
- arules
- elasticnet - Net and also provides functions for doing sparse PCA." |
- glmnet - net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression." |
- rpart
- Hugging Face, BiasAware: Dataset Bias Detection
- TensorBoard Projector
- What-if Tool
- algofairness
- Bayesian Case Model
- Bayesian Rule List (BRL)
- cdt15, Causal Discovery Lab., Shiga University - Gaussianity of the data." |
- Falling Rule List (FRL)
- Grad-CAM - CAM is a technique for making convolutional neural networks more transparent by visualizing the regions of input that are important for predictions in computer vision models. |
- Sparse Principal Components (GLRM)
- Monotonic
- parity-fairness
- ProtoPNet - duke?style=social) | "This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen (Duke University), Oscar Li| (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University).” |
- rationale
- Decision Trees - parametric supervised learning method used for classification and regression.” |
- Generalized Linear Models
- scikit-multiflow
- text_explainability - known state-of-the-art explainability approaches for text can be composed.” |
- text_sensitivity
- ALEPlot - order interaction effects in black-box supervised learning models." |
- DALEXtra: Extension for 'DALEX' Package
- interpret: Fit Interpretable Machine Learning Models
- fairness
- forestmodel
- fscaret
- gam
- glm2
- Penalized Generalized Linear Models
- Monotonic GBM
- Sparse Principal Components (GLRM)
- ICEbox: Individual Conditional Expectation Plot Toolbox
- live
- modelDown
- modelOriented - based MI².AI. |
- quantreg
- RuleFit
- Scalable Bayesian Rule Lists (SBRL)
- shapper
- smbinning
- Scikit-Explain - friendly Python module for machine learning explainability," featuring PD and ALE plots, LIME, SHAP, permutation importance and Friedman's H, among other methods. |
- LDNOOBW
- RuleFit
- fairness-comparison -  | "meant to facilitate the benchmarking of fairness aware machine learning algorithms.” |
- BlackBoxAuditing -  | "Research code for auditing and exploring black box machine-learning models.” |
- themis-ml -  | "A Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms.” |
- fairml -  | "a python toolbox auditing the machine learning models for bias.” |
- Themis -  | "A testing-based approach for measuring discrimination in a software system.” |
- Explainable Boosting Machine EBM/GA2M -  | "an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions.” |
- vip -  | "An R package for constructing variable importance plots (VIPs)." |
- tensorflow/lucid -  | "a collection of infrastructure and tools for research in neural network interpretability.” |
- tensorflow/model-analysis -  | "a library for evaluating TensorFlow models. It allows users to evaluate their models on large amounts of data in a distributed manner, using the same metrics defined in their trainer. These metrics can be computed over different slices of data and visualized in Jupyter notebooks.” |
- ingredients -  | "A collection of tools for assessment of feature importance and feature effects."|
- DrWhyAI -  | "DrWhy is [a] collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models." |
- iBreakDown -  | "A model agnostic tool for explanation of predictions from black boxes ML models."|
- dalex -  | "moDel Agnostic Language for Exploration and eXplanation.” |
- xgboostExplainer -  | "An R package that makes xgboost models fully interpretable. |
- PAIR-code - facets -  | "Visualizations for machine learning datasets." |
- Keras-vis -  | "a high-level toolkit for visualizing and debugging your trained keras neural net models.” |
- tensorflow/model-card-toolkit -  | "streamlines and automates generation of Model Cards, machine learning documents that provide context and transparency into a model's development and performance. Integrating the MCT into your ML pipeline enables you to share model metadata and metrics with researchers, developers, reporters, and more.” |
- yellowbrick -  | "A suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process.” |
- xai -  | "A Machine Learning library that is designed with AI explainability in its core.” |
- cleverhans -  | "An adversarial example library for constructing attacks, building defenses, and benchmarking both.” |
- allennlp -  | "An open-source NLP research library, built on PyTorch.” |
- ydata-profiling -  | "Provide(s) a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution.” |
- lime -  | "explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations).” |
- TensorWatch -  | "a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data.” |
- xdeep -  | "An open source Python library for Interpretable Machine Learning.” |
- tf-explain -  | "Implements interpretability methods as Tensorflow 2.x callbacks to ease neural network's understanding.” |
- tensorflow/lattice -  | "a library that implements constrained and interpretable lattice based models. It is an implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow.” |
- MindsDB -  | "enables developers to build AI tools that need access to real-time data to perform their tasks.” |
- DiCE -  | "Generate Diverse Counterfactual Explanations for any machine learning model.” |
- fairlearn -  | "a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage.” |
- causalml -  | "Uplift modeling and causal inference with machine learning algorithms.” |
- captum -  | "Model interpretability and understanding for PyTorch.” |
- Causal Discovery Toolbox -  | "Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.” |
- iNNvestigate neural nets -  | A comprehensive Python library to analyze and interpret neural network behaviors in Keras, featuring a variety of methods like Gradient, LRP, and Deep Taylor. |
- Optimal Sparse Decision Trees -  | "This accompanies the paper, ["Optimal Sparse Decision Trees"](https://arxiv.org/abs/1904.12847) by Xiyang Hu, Cynthia Rudin, and Margo Seltzer.” |
- sklearn-expertsys -  | "a scikit-learn compatible wrapper for the Bayesian Rule List classifier developed by Letham et al., 2015, extended by a minimum description length-based discretizer (Fayyad & Irani, 1993) for continuous data, and by an approach to subsample large datasets for better performance.” |
- imodels -  | "Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use.” |
- pyGAM -  | "Generalized Additive Models in Python.” |
- shapley -  | "a Python library for evaluating binary classifiers in a machine learning ensemble.” |
- ml_privacy_meter -  | "an open-source library to audit data privacy in statistical and machine learning algorithms. The tool can help in the data protection impact assessment process by providing a quantitative analysis of the fundamental privacy risks of a (machine learning) model.” |
- AI Fairness 360 -  | "A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.” |
- aequitas -  | "Aequitas is an open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive tools.” |
- deepvis -  | "the code required to run the Deep Visualization Toolbox, as well as to generate the neuron-by-neuron visualizations using regularized optimization.” |
- Alibi -  | "Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.” |
- pytorch-grad-cam -  | "a package with state of the art methods for Explainable AI for computer vision. This can be used for diagnosing model predictions, either in production or while developing models. The aim is also to serve as a benchmark of algorithms and metrics for research of new explainability methods.” |
- tensorflow/privacy -  | "the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided.” |
- manifold -  | "A model-agnostic visual debugging tool for machine learning." |
- PiML-Toolbox -  | "a new Python toolbox for interpretable machine learning model development and validation. Through low-code interface and high-code APIs, PiML supports a growing list of inherently interpretable ML models.” |
- xplique -  | "A Python toolkit dedicated to explainability. The goal of this library is to gather the state of the art of Explainable AI to help you understand your complex neural network models.” |
- dtreeviz -  | "A python library for decision tree visualization and model interpretation.” |
- explainerdashboard -  | "Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.” |
- explainX -  | "Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.” |
- gplearn -  | "implements Genetic Programming in Python, with a scikit-learn inspired and compatible API.” |
- ecco -  | "Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).” |
- REVISE: REvealing VIsual biaSEs -  | "A tool that automatically detects possible forms of bias in a visual dataset along the axes of object-based, attribute-based, and geography-based patterns, and from which next steps for mitigation are suggested.” |
- ExplainaBoard -  | "a tool that inspects your system outputs, identifies what is working and what is not working, and helps inspire you with ideas of where to go next.” |
- PDPbox -  | "Python Partial Dependence Plot toolbox. Visualize the influence of certain features on model predictions for supervised machine learning algorithms, utilizing partial dependence plots.” |
- Giskard -  | "The testing framework dedicated to ML models, from tabular to LLMs. Scan AI models to detect risks of biases, performance issues and errors. In 4 lines of code.” |
- anchor -  | "Code for 'High-Precision Model-Agnostic Explanations' paper.” |
- foolbox -  | "A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX.” |
- TextFooler -  | "A Model for Natural Language Attack on Text Classification and Inference"
- casme -  | "contains the code originally forked from the ImageNet training in PyTorch that is modified to present the performance of classifier-agnostic saliency map extraction, a practical algorithm to train a classifier-agnostic saliency mapping by simultaneously training a classifier and a saliency mapping.” |
- ContrastiveExplanation - Foil Trees -  | "provides an explanation for why an instance had the current outcome (fact) rather than a targeted outcome of interest (foil). These counterfactual explanations limit the explanation to the features relevant in distinguishing fact from foil, thereby disregarding irrelevant features.” |
- DeepLIFT -  | "This repository implements the methods in 'Learning Important Features Through Propagating Activation Differences' by Shrikumar, Greenside & Kundaje, as well as other commonly-used methods such as gradients, gradient-times-input (equivalent to a version of Layerwise Relevance Propagation for ReLU networks), guided backprop and integrated gradients.” |
- eli5 -  | "A library for debugging/inspecting machine learning classifiers and explaining their predictions.” |
- Integrated-Gradients -  | "a variation on computing the gradient of the prediction output w.r.t. features of the input. It requires no modification to the original network, is simple to implement, and is applicable to a variety of deep models (sparse and dense, text and vision).” |
- L2X -  | "Code for replicating the experiments in the paper [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://arxiv.org/pdf/1802.07814.pdf) at ICML 2018, by Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan.” |
- lofo-importance -  | "LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, with a validation scheme of choice, based on the chosen metric.” |
- pyBreakDown -  | See [dalex](https://dalex.drwhy.ai/). |
- responsibly -  | "Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems.” |
- treeinterpreter -  | "Package for interpreting scikit-learn's decision tree and random forest predictions.” |
- woe -  | "Tools for WoE Transformation mostly used in ScoreCard Model for credit rating.” |
- PyCEbox -  | "Python Individual Conditional Expectation Plot Toolbox.” |
- skope-rules -  | "a Python machine learning module built on top of scikit-learn and distributed under the 3-Clause BSD license.” |
- checklist -  | "Beyond Accuracy: Behavioral Testing of NLP models with CheckList.” |
- SALib -  | "Python implementations of commonly used sensitivity analysis methods. Useful in systems modeling to calculate the effects of model inputs or exogenous factors on outputs of interest.” |
- solas-ai-disparity -  | "a collection of tools that allows modelers, compliance, and business stakeholders to test outcomes for bias or discrimination using widely accepted fairness metrics.” |
- DIANNA -  | "DIANNA is a Python package that brings explainable AI (XAI) to your research project. It wraps carefully selected XAI methods in a simple, uniform interface. It's built by, with and for (academic) researchers and research software engineers working on machine learning projects.” |
- fastshap -  | "The goal of fastshap is to provide an efficient and speedy approach (at least relative to other implementations) for computing approximate Shapley values, which help explain the predictions from any machine learning model." |
- flashlight -  | "The goal of this package is [to] shed light on black box machine learning models." |
- OptBinning -  | "a library written in Python implementing a rigorous and flexible mathematical programming formulation to solve the optimal binning problem for a binary, continuous and multiclass target type, incorporating constraints not previously addressed.” |
- ml-fairness-gym -  | "a set of components for building simple simulations that explore the potential long-run impacts of deploying machine learning-based decision systems in social environments.” |
- Quantus -  | "Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations." |
- robustness -  | "a package we (students in the [MadryLab](http://madry-lab.ml/)) created to make training, evaluating, and exploring neural networks flexible and easy.” |
- RISE -  | "contains source code necessary to reproduce some of the main results in the paper: [Vitali Petsiuk](http://cs-people.bu.edu/vpetsiuk/), [Abir Das](http://cs-people.bu.edu/dasabir/), [Kate Saenko](http://ai.bu.edu/ksaenko.html) (BMVC, 2018) [and] [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://arxiv.org/abs/1806.07421).” |
- DeepExplain -  | "provides a unified framework for state-of-the-art gradient and perturbation-based attribution methods. It can be used by researchers and practitioners for better undertanding the recommended existing models, as well for benchmarking other attribution methods.” |
- tensorflow/fairness-indicators -  | "designed to support teams in evaluating, improving, and comparing models for fairness concerns in partnership with the broader Tensorflow toolkit.” |
- explabox -  | "aims to support data scientists and machine learning (ML) engineers in explaining, testing and documenting AI/ML models, developed in-house or acquired externally. The explabox turns your ingestibles (AI/ML model and/or dataset) into digestibles (statistics, explanations or sensitivity insights).” |
- modelStudio -  | "Automates the explanatory analysis of machine learning predictive models."|
- tensorflow/tcav -  | "Testing with Concept Activation Vectors (TCAV) is a new interpretability method to understand what signals your neural networks models uses for prediction.” |
- Born-again Tree Ensembles -  | "Born-Again Tree Ensembles: Transforms a random forest into a single, minimal-size, tree with exactly the same prediction function in the entire feature space (ICML 2020)." |)
- lime -  | "R port of the Python lime package."|
- lit -  | "The Learning Interpretability Tool (LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks.” |
- CalculatedContent, WeightWatcher -  | "The WeightWatcher tool for predicting the accuracy of Deep Neural Networks." |
- tensorflow/model-remediation -  | "a library that provides solutions for machine learning practitioners working to create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.” |
- scikit-fairness -  | Historical link. Merged with [fairlearn](https://fairlearn.org/). |
- learning-fair-representations -  | "Python numba implementation of Zemel et al. 2013 <http://www.cs.toronto.edu/~toni/Papers/icml-final.pdf>" |
- debiaswe -  | "Remove problematic gender bias from word embeddings.” |
- pySS3 -  | "The SS3 text classifier is a novel and simple supervised machine learning model for text classification which is interpretable, that is, it has the ability to naturally (self)explain its rationale.” |
- LangFair -  | "LangFair is a Python library for conducting use-case level LLM bias and fairness assessments"
- DoWhy -  | "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.” |
- shap -  | "a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions"
- shapFlex -  | Computes stochastic Shapley values for machine learning models to interpret them and evaluate fairness, including causal constraints in the feature space. |
- pymc3 -  | "PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.” |
- langtest -  | "LangTest: Deliver Safe & Effective Language Models" |
- effector -  | "eXplainable AI for Tabular Data" |
- DiscriLens -  | "Discrimination in Machine Learning." |
- PAIR-code - datacardsplaybook -  | "The Data Cards Playbook helps dataset producers and publishers adopt a people-centered approach to transparency in dataset documentation." |
- PAIR-code - knowyourdata -  | "A tool to help researchers and product teams understand datasets with the goal of improving data quality, and mitigating fairness and bias issues." |
- TensorBoard Projector
- Certifiably Optimal RulE ListS -  | "CORELS is a custom discrete optimization technique for building rule lists over a categorical feature space." |
- Secure-ML -  | "Secure Linear Regression in the Semi-Honest Two-Party Setting." |
- acd -  | "Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Official code for *Hierarchical interpretations for neural network predictions*.” |
- ALEPython -  | "Python Accumulated Local Effects package.” |
- Aletheia -  | "A Python package for unwrapping ReLU DNNs.” |
- Bayesian Ors-Of-Ands -  | "This code implements the Bayesian or-of-and algorithm as described in the BOA paper. We include the tictactoe dataset in the correct formatting to be used by this code.” |
- fair-classification -  | "Python code for training fair logistic regression classifiers.” |
- fairness_measures_code -  | "contains implementations of measures used to quantify discrimination.” |
- H2O-3 Monotonic GBM
- h2o-LLM-eval -  | "Large-language Model Evaluation framework with Elo Leaderboard and A-B testing." |
- hate-functional-tests -  | HateCheck: A dataset and test suite from an ACL 2021 paper, offering functional tests for hate speech detection models, including extensive case annotations and testing functionalities. |
- interpret_with_rules -  | "induces rules to explain the predictions of a trained neural network, and optionally also to explain the patterns that the model captures from the training data, and the patterns that are present in the original dataset.” |
- InterpretME -  | "integrates knowledge graphs (KG) with machine learning methods to generate interesting meaningful insights. It helps to generate human- and machine-readable decisions to provide assistance to users and enhance efficiency.” |
- keract -  | Keract is a tool for visualizing activations and gradients in Keras models; it's meant to support a wide range of Tensorflow versions and to offer an intuitive API with Python examples. |
- LiFT -  | "The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness and the mitigation of bias in large-scale machine learning workflows. The measurement module includes measuring biases in training data, evaluating fairness metrics for ML models, and detecting statistically significant differences in their performance across different subgroups.” |
- lilac -  | "Curate better data for LLMs." |
- lrp_toolbox -  | "The Layer-wise Relevance Propagation (LRP) algorithm explains a classifer's prediction specific to a given data point by attributing relevance scores to important components of the input by using the topology of the learned model itself.” |
- MLextend - to-day data science tasks.” |
- mllp -  | "This is a PyTorch implementation of Multilayer Logical Perceptrons (MLLP) and Random Binarization (RB) method to learn Concept Rule Sets (CRS) for transparent classification tasks, as described in our paper: [Transparent Classification with Multilayer Logical Perceptrons and Random Binarization](https://arxiv.org/abs/1912.04695).” |
- Monotonic Constraints
- parity-fairness
- pjsaelin / Cubist -  | "A Python package for fitting Quinlan's Cubist regression model" |
- Privacy-Preserving-ML -  | "Implementation of privacy-preserving SVM assuming public model private data scenario (data in encrypted but model parameters are unencrypted) using adequate partial homomorphic encryption.” |
- ProtoPNet -  | "This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen (Duke University), Oscar Li| (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University).” |
- pytorch-innvestigate -  | "PyTorch implementation of Keras already existing project: [https://github.com/albermax/innvestigate/](https://github.com/albermax/innvestigate/).” |
- Risk-SLIM -  | "a machine learning method to fit simple customized risk scores in python.” |
- SAGE -  | "SAGE (Shapley Additive Global importancE) is a game-theoretic approach for understanding black-box machine learning models. It quantifies each feature's importance based on how much predictive power it contributes, and it accounts for complex feature interactions using the Shapley value.” |
- Super-sparse Linear Integer models - SLIMs -  | "a package to learn customized scoring systems for decision-making problems.” |
- tensorfuzz -  | "a library for performing coverage guided fuzzing of neural networks.” |
- text_explainability - known state-of-the-art explainability approaches for text can be composed.” |
- text_sensitivity
- TRIAGE -  | "This repository contains the implementation of TRIAGE, a "Data-Centric AI" framework for data characterization tailored for regression.” |
- XGBoost
- Causal SVM -  | "We present a new machine learning approach to estimate whether a treatment has an effect on an individual, in the setting of the classical potential outcomes framework with binary outcomes." |
- ExplainPrediction -  | "Generates explanations for classification and regression models and visualizes them." |
- fairmodels -  | "Flexible tool for bias detection, visualization, and mitigation. Use models explained with DALEX and calculate fairness classification metrics based on confusion matrices using fairness_check() or try newly developed module for regression models using fairness_check_regression()." |
- featureImportance -  | "An extension for the mlr package and allows to compute the permutation feature importance in a model-agnostic manner." |
- H2O-3 Monotonic GBM
- H2O-3 Penalized Generalized Linear Models
- H2O-3 Sparse Principal Components
- iml -  | "An R package that interprets the behavior and explains predictions of machine learning models."|
- lightgbmExplainer -  | "An R package that makes LightGBM models fully interpretable."|
- mcr -  | "An R package for Model Reliance and Model Class Reliance."|
- shapleyR -  | "An R package that provides some functionality to use mlr tasks and models to generate shapley values." |
- contextual-AI -  | "Contextual AI adds explainability to different stages of machine learning pipelines | data, training, and inference | thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.” |
- counterfit -  | "a CLI that provides a generic automation layer for assessing the security of ML models.” |
- TorchUncertainty -  | "A package designed to help you leverage uncertainty quantification techniques and make your deep neural networks more reliable.” |
-
Common or Useful Datasets
- Data.gov
- Adult income dataset
- COMPAS Recidivism Risk Score Data and Analysis
- All Lending Club loan data
- Amazon Open Data
- Home Mortgage Disclosure Act (HMDA) Data
- MIMIC-III Clinical Database
- UCI ML Data Repository
- FANNIE MAE Single Family Loan Performance
- NYPD Stop, Question and Frisk Data
- Statlog (German Credit Data)
- Wikipedia Talk Labels: Personal Attacks
- Have I Been Trained?
- Presidential Deepfakes Dataset
- Bruegel, A dataset on EU legislation for the digital world
- Balanced Faces in the Wild - bias-bfw?style=social)
- nikhgarg / EmbeddingDynamicStereotypes
- socialfoundations / folktables
- COMPAS Recidivism Risk Score Data and Analysis
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Machine Learning Environment Management Tools
- mlflow
- dvc
- gigantum - driven science." |
- mlmd -  | "For recording and retrieving metadata associated with ML developer and data scientist workflows." |
- modeldb -  | "Open Source ML Model Versioning, Metadata, and Experiment Management." |
- Opik -  | "Evaluate, test, and ship LLM applications across your dev and production lifecycles." |
- neptune
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Benchmarks
- Sociotechnical Safety Evaluation Repository
- HELM
- Nvidia MLPerf
- OpenML Benchmarking Suites
- Real Toxicity Prompts (Allen Institute for AI)
- GEM
- TrustLLM-Benchmark
- Trust-LLM-Benchmark Leaderboard
- SafetyPrompts.com
- WAVES: Benchmarking the Robustness of Image Watermarks
- MLCommons, MLCommons AI Safety v0.5 Proof of Concept
- ML.ENERGY Leaderboard - tuned ones, can generate human-like responses to chat prompts. Using Zeus for energy measurement, we created a leaderboard for LLM chat energy consumption." |
- benchm-ml -  | "A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.)." |
- Hugging Face, evaluate -  | "Evaluate: A library for easily evaluating machine learning models and datasets." |
- EleutherAI, Language Model Evaluation Harness -  | "A framework for few-shot evaluation of language models." |
- TruthfulQA -  | "TruthfulQA: Measuring How Models Imitate Human Falsehoods." |
- DecodingTrust -  | "A Comprehensive Assessment of Trustworthiness in GPT Models." |
- Bias Benchmark for QA dataset-BBQ -  | "Repository for the Bias Benchmark for QA dataset." |
- jphall663, Generative AI Risk Management Resources -  | "A place for ideas and drafts related to GAI risk management." |
- Winogender Schemas -  | "Data for evaluating gender bias in coreference resolution systems." |
- MLCommons, AI Luminate: A collaborative, transparent approach to safer AI
- Evidently AI 100+ LLM benchmarks and evaluation datasets
- i-gallegos, Fair-LLM-Benchmark -  | Benchmark from "Bias and Fairness in Large Language Models: A Survey" |
- MLCommons, Introducing v0.5 of the AI Safety Benchmark from MLCommons
- ModelSlant.com
- OpenML Benchmarking Suites
- Real Toxicity Prompts - Allen Institute for AI
- Wild-Time: A Benchmark of in-the-Wild Distribution Shifts over Time -  | "Benchmark for Natural Temporal Distribution Shift (NeurIPS 2022)." |
- yandex-research - tabred -  | "A Benchmark of Tabular Machine Learning in-the-Wild with real-world industry-grade tabular datasets." |
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Personal Data Protection Tools
- LLM Dataset Inference: Did you train on my dataset? -  | "Official Repository for Dataset Inference for LLMs" |
-
Archived
- Artificial Intelligence and Worker Well-Being: Principles and Best Practices for Developers and Employers
- Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People, HTML
- CISA Roadmap for Artificial Intelligence 2023 2024
- Data Availability and Transparency Act 2022
- Developing Financial Sector Resilience in a Digital World: Selected Themes in Technology and Related Risks
- Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
- FACT SHEET: Biden-Harris Administration Announces New AI Actions and Receives Additional Major Voluntary Commitment on AI
- FACT SHEET: Biden-Harris Administration Outlines Coordinated Approach to Harness Power of AI for U.S. National Security
- FACT SHEET: Biden-Harris Administration Secures Voluntary Commitments from Leading Artificial Intelligence Companies to Manage the Risks Posed by AI
- FACT SHEET: Biden-Harris Administration Takes New Steps to Advance Responsible Artificial Intelligence Research, Development, and Deployment
- FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence
- Federal Register of Legislation, Data Availability and Transparency Act 2022
- Generative Artificial Intelligence Lexicon
- Generative Artificial Intelligence Risk Assessment SIMM 5305-F
- Generative Artificial Intelligence Risk Assessment SIMM 5305-F February 2025 update
- Guidelines on the Application of Republic Act No. 10173 or the Data Privacy Act of 2012 DPA, Its Implementing Rules and Regulations, and the Issuances of the Commission to Artificial Intelligence Systems Processing Personal Data NPC Advisory No. 2024-04
- Introducing the DATA Scheme
- M-21-06 Memorandum for the Heads of Executive Departments and Agencies, Guidance for Regulation of Artificial Intelligence Applications
- M-24-18 Memorandum for the Heads of Executive Departments and Agencies, Advancing the Responsible Acquisition of Artificial Intelligence in Government
- Memorandum on Advancing the United States’ Leadership in Artificial Intelligence; Harnessing Artificial Intelligence to Fulfill National Security Objectives; and Fostering the Safety, Security, and Trustworthiness of Artificial Intelligence
- National Artificial Intelligence Research and Development Strategic Plan 2023 Update
- National Science and Technology Council
- Office of Science and Technology Policy
- Aiming for truth, fairness, and equity in your company’s use of AI
- Using Artificial Intelligence and Algorithms
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Community and Official Guidance Resources
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Official Policy, Frameworks, and Guidance
- U.S. Web Design System (USWDS) Design principles
- Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
- 12 CFR Part 1002 - Equal Credit Opportunity Act (Regulation B)
- Algorithmic Accountability Act of 2023
- Algorithm Charter for Aotearoa New Zealand
- A Regulatory Framework for AI: Recommendations for PIPEDA Reform
- Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment - Shaping Europe’s digital future - European Commission
- Audit of Governance and Protection of Department of Defense Artificial Intelligence Data and Technology
- Commodity Futures Trading Commission (CFTC), A Primer on Artificial Intelligence in Securities Markets
- Biometric Information Privacy Act
- Booker Wyden Health Care Letters
- California Consumer Privacy Act (CCPA)
- California Department of Justice, How to Read a Privacy Policy
- California Privacy Rights Act (CPRA)
- 2023-08-16 Can’t lose what you never had: Claims about digital ownership and creation in the age of generative AI
- Children's Online Privacy Protection Rule ("COPPA")
- Civil liability regime for artificial intelligence
- Congressional Research Service, Artificial Intelligence: Overview, Recent Advances, and Considerations for the 118th Congress
- Consumer Data Protection Act (Code of Virginia)
- DARPA, Explainable Artificial Intelligence (XAI) (Archived)
- Data Availability and Transparency Act 2022 (Australia)
- data.gov, Privacy Policy and Data Policy
- Defense Technical Information Center, Computer Security Technology Planning Study, October 1, 1972
- Department for Science, Innovation and Technology, Frontier AI: capabilities and risks - discussion paper (United Kingdom)
- United States Department of Commerce, Intellectual property
- United States Department of Defense, AI Principles: Recommendations on the Ethical Use of Artificial Intelligence
- United States Department of Defense, Chief Data and Artificial Intelligence Officer (CDAO) Assessment and Assurance
- RAI Toolkit
- Developing Financial Sector Resilience in a Digital World: Selected Themes in Technology and Related Risks
- The Digital Services Act package (EU Digital Services Act and Digital Markets Act)
- Directive on Automated Decision Making (Canada)
- EEOC Letter (from U.S. senators re: hiring software)
- European Commission, Hiroshima Process International Guiding Principles for Advanced AI system
- Executive Order 13960 (2020-12-03), Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government
- Facial Recognition and Biometric Technology Moratorium Act of 2020
- FDA Artificial Intelligence/Machine Learning (AI/ML)-Based: Software as a Medical Device (SaMD) Action Plan, updated January 2021
- FDA Software as a Medical Device (SAMD) guidance (December 8, 2017)
- FDIC Supervisory Guidance on Model Risk Management
- Federal Consumer Online Privacy Rights Act (COPRA)
- Federal Reserve Bank of Dallas, Regulation B, Equal Credit Opportunity, Credit Scoring Interpretations: Withdrawl of Proposed Business Credit Amendments, June 3, 1982
- FHA model risk management/model governance guidance
- FTC Business Blog
- 2021-01-11 Facing the facts about facial recognition
- 2021-04-19 Aiming for truth, fairness, and equity in your company’s use of AI
- 2022-07-11 Location, health, and other sensitive information: FTC committed to fully enforcing the law against illegal use and sharing of highly sensitive data
- 2023-07-25 Protecting the privacy of health information: A baker’s dozen takeaways from FTC cases
- 2023-08-22 For business opportunity sellers, FTC says “AI” stands for “allegedly inaccurate”
- 2023-09-15 Updated FTC-HHS publication outlines privacy and security laws and rules that impact consumer health data
- 2023-09-18 Companies warned about consequences of loose use of consumers’ confidential data
- 2023-09-27 Could PrivacyCon 2024 be the place to present your research on AI, privacy, or surveillance?
- 2022-05-20 Security Beyond Prevention: The Importance of Effective Breach Disclosures
- 2023-02-01 Security Principles: Addressing underlying causes of risk in complex systems
- 2023-06-29 Generative AI Raises Competition Concerns
- 2023-12-19 Coming face to face with Rite Aid’s allegedly unfair use of facial recognition technology
- FTC Privacy Policy
- Government Accountability Office: Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities
- General Data Protection Regulation (GDPR)
- Article 22 EU GDPR "Automated individual decision-making, including profiling"
- General principles for the use of Artificial Intelligence in the financial sector
- Gouvernance des algorithmes d’intelligence artificielle dans le secteur financier (France)
- Guidelines for secure AI system development
- Innovation spotlight: Providing adverse action notices when using AI/ML models
- Justice in Policing Act
- National Conference of State Legislatures (NCSL) 2020 Consumer Data Privacy Legislation
- National Institute of Standards and Technology (NIST), AI 100-1 Artificial Intelligence Risk Management Framework (NIST AI RMF 1.0)
- National Institute of Standards and Technology (NIST), Four Principles of Explainable Artificial Intelligence, Draft NISTIR 8312, 2020-08-17
- National Institute of Standards and Technology (NIST), Four Principles of Explainable Artificial Intelligence, NISTIR 8312, 2021-09-29
- National Institute of Standards and Technology (NIST), Measurement Uncertainty
- International Bureau of Weights and Measures (BIPM), Evaluation of measurement data—Guide to the expression of uncertainty in measurement
- National Institute of Standards and Technology (NIST), NIST Special Publication 800-30 Revision 1, Guide for Conducting Risk Assessments
- National Science and Technology Council (NSTC), Select Committee on Artificial Intelligence, National Artificial Intelligence Research and Development Strategic Plan 2023 Update
- New York City Automated Decision Systems Task Force Report (November 2019)
- OECD, Open, Useful and Re-usable data (OURdata) Index: 2019 - Policy Paper
- Office of the Director of National Intelligence (ODNI), The AIM Initiative: A Strategy for Augmenting Intelligence Using Machines
- Office of Management and Budget, Guidance for Regulation of Artificial Intelligence Applications, finalized November 2020
- Office of Science and Technology Policy, Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People
- Office of the Comptroller of the Currency (OCC), 2021 Model Risk Management Handbook
- Online Harms White Paper: Full government response to the consultation (United Kingdom)
- Online Privacy Act of 2023
- Online Safety Bill (United Kingdom)
- Principles of Artificial Intelligence Ethics for the Intelligence Community
- Privacy Act 1988 (Australia)
- Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)
- Amendments adopted by the European Parliament on 14 June 2023 on the proposal for a regulation of the European Parliament and of the Council on laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts
- Psychological Foundations of Explainability and Interpretability in Artificial Intelligence
- The Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018 (United Kingdom)
- Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures
- Questions from the Commission on Protecting Privacy and Preventing Discrimination
- RE: Use of External Consumer Data and Information Sources in Underwriting for Life Insurance
- Singapore Personal Data Protection Commission (PDPC), Companion to the Model AI Governance Framework – Implementation and Self-Assessment Guide for Organizations
- Singapore Personal Data Protection Commission (PDPC), Compendium of Use Cases: Practical Illustrations of the Model AI Governance Framework
- Singapore Personal Data Protection Commission (PDPC), Model Artificial Intelligence Governance Framework (Second Edition)
- Supervisory Guidance on Model Risk Management
- Testing the Reliability, Validity, and Equity of Terrorism Risk Assessment Instruments
- UNESCO, Artificial Intelligence: examples of ethical dilemmas
- AI Risk Management Playbook (AIRMP)
- AI Use Case Inventory (DOE Use Cases Releasable to Public in Accordance with E.O. 13960)
- Digital Climate Solutions Inventory
- United States Department of Homeland Security, Use of Commercial Generative Artificial Intelligence (AI) Tools
- United States Department of Justice, Privacy Act of 1974
- United States Department of Justice, Overview of The Privacy Act of 1974 (2020 Edition)
- United States Patent and Trademark Office (USPTO), Public Views on Artificial Intelligence and Intellectual Property Policy
- U.S. Army Concepts Analysis Agency, Proceedings of the Thirteenth Annual U.S. Army Operations Research Symposium, Volume 1, October 29 to November 1, 1974
- National Institute of Standards and Technology
- Department for Science, Innovation and Technology, The Bletchley Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023
- Using Artificial Intelligence and Algorithms
- Consumer Financial Protection Bureau (CFPB), Chatbots in consumer finance
- Department for Science, Innovation and Technology, Guidance, Introduction to AI Assurance
- National Security Commission on Artificial Intelligence, Final Report
- (Draft Guideline) E-23 – Model Risk Management
- Office of the United Nations High Commissioner for Human Rights
- United States Department of Energy Artificial Intelligence and Technology Office
- Securities and Exchange Commission, SEC Charges Two Investment Advisers with Making False and Misleading Statements About Their Use of Artificial Intelligence
- National Telecommunications and Information Administration, AI Accountability Policy Report
- United States Department of the Treasury, Managing Artificial Intelligence-Specific Cybersecurity Risks in the Financial Services Sector, March 2024
- State of California, Department of Technology, Office of Information Security, Generative Artificial Intelligence Risk Assessment, SIMM 5305-F, March 2024
- United States Department of Commerce Internet Policy Task Force, Commercial Data Privacy and Innovation in the Internet Economy: A Dynamic Policy Framework
- The White House, Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy, February 2012
- United States Department of Energy Artificial Intelligence and Technology Office
- OECD.AI, The Bias Assessment Metrics and Measures Repository
- Bundesamt für Sicherheit in der Informationstechnik, Generative AI Models - Opportunities and Risks for Industry and Authorities
- California Department of Technology, GenAI Executive Order
- Commodity Futures Trading Commission (CFTC), Responsible Artificial Intelligence in Financial Markets
- Mississippi Department of Education, Artificial Intelligence Guidance for K-12 Classrooms
- National Security Agency, Central Security Service, Artificial Intelligence Security Center
- United States Department of Homeland Security, Safety and Security Guidelines for Critical Infrastructure Owners and Operators
- Autoriteit Persoonsgegevens, scraping bijna altijd illegal (Dutch Data Protection Authority, "Scraping is always illegal")
- NATO, Narrative Detection and Topic Modelling in the Baltics
- Department for Science, Innovation and Technology and AI Safety Institute, International Scientific Report on the Safety of Advanced AI
- National Physical Laboratory (NPL), Beginner's guide to measurement GPG118
- National Institute of Standards and Technology (NIST), Assessing Risks and Impacts of AI (ARIA)
- AI Safety Institute (AISI), Advanced AI evaluations at AISI: May update
- Colorado General Assembly, SB24-205 Consumer Protections for Artificial Intelligence, Concerning consumer protections in interactions with artificial intelligence systems"
- European Data Protection Supervisor, First EDPS Orientations for EUIs using Generative AI
- Health Canada, Transparency for machine learning-enabled medical devices: Guiding principles
- National framework for the assurance of artificial intelligence in government (Australia)
- Callaghan Innovation, EU AI Fact Sheet 4, High-risk AI systems
- European Data Protection Board (EDPB), AI Auditing documents
- European Labour Authority (ELA), Artificial Intelligence and Algorithms in Risk Assessment: Addressing Bias, Discrimination and other Legal and Ethical Issues
- OECD, AI, data governance and privacy: Synergies and areas of international co-operation
- Generative Artificial Intelligence Reference Guide
- Algorithmic Impact Assessment tool
- European Parliament, The impact of the General Data Protection Regulation (GDPR) on artificial intelligence
- Singapore Personal Data Protection Commission (PDPC), Privacy Enhancing Technology (PET): Proposed Guide on Synthetic Data Generation
- Office of Educational Technology, Designing for Education with Artificial Intelligence: An Essential Guide for Developers
- Examining Proposed Uses of LLMs to Produce or Assess Assurance Arguments
- The Artificial Intelligence and Data Act Companion document
- OECD Digital Economy Papers, No. 341, November 2022, Measuring the Environmental Impacts of Artificial Intelligence Computer and Applications: The AI Footprint
- Ethics Guidelines for Trustworthy AI - Level Expert Group on Artificial Intelligence
- The AI Impact Navigator
- Copyright and Artificial Intelligence Part 2 Copyrightability
- AI Governance: Leadership insights and the Voluntary AI Safety Standard in practice
- Artificial Intelligence Model Clauses
- Australia’s AI Ethics Principles
- Guidance for AI Adoption
- Guidance for AI Adoption: Foundations v1.0
- Guidance for AI Adoption: Implementation practices v1.0
- Introducing mandatory guardrails for AI in high-risk settings: proposals paper
- Voluntary AI Safety Standard
- Evaluation of the whole-of-government trial of Microsoft 365 Copilot: Summary of evaluation findings
- Policy for the responsible use of AI in government
- Guidance on privacy and developing and training generative AI models
- Guidance on privacy and the use of commercially available AI products
- Technical standard for government’s use of artificial intelligence
- Understanding Responsibilities in AI Practices
- An Act to enact the Consumer Privacy Protection Act, the Personal Information and Data Protection Tribunal Act and the Artificial Intelligence and Data Act and to make consequential and related amendments to other Acts
- AI in Canada
- Artificial Intelligence and Data Act
- Responsible use of artificial intelligence in government
- 人工智能全球治理行动计划
- Presidency of the Republic of Colombia, Marco Ético para la Inteligencia Artificial en Colombia
- Ministerio de Ciencia, Innovación, Tecnología y Telecomunicaciones
- Etički kodeks za pripremu i provedbu projekata financiranih projektom Digitalne, inovativne i zelene tehnologije, DIGIT PROJEKT
- Digital Croatia Strategy for the period until 2032
- Nacionalni program zaštite potrošača za razdoblje do 2028. godine
- Pametna sigurnost: Praktična primjena umjetne inteligencije i nosivih senzora u građevinarstvu
- Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence Volume 1 Croatia
- PDF here
- National Strategy for Artificial Intelligence
- Finland's Age of Artificial Intelligence: Turning Finland into a leading country in the application of artificial intelligence. Objective and recommendations for measures
- Challenges and opportunities of artificial intelligence in the fight against information manipulation
- German-French recommendations for the use of AI programming assistants
- Germany AI Strategy Report
- OECD-Bericht zu Künstlicher Intelligenz in Deutschland
- Recommendations of the Data Ethics Commission for the Federal Government's Strategy on Artificial Intelligence,
- Artificial Intelligence: Model Personal Data Protection Framework
- Aðgerðaáætlun um gervigreind 2024-2026 - 2026 | November 2024
- Efnahagsleg tækifæri gervigreindar á Íslandi
- AI Governance Framework for India 2025-26
- Stakeholders consultation on "Draft Standard for the Schema and Taxonomy of an AI Incident Database in Telecommunications and Critical Digital Infrastructure"
- AI - Here for Good: A National Artificial Intelligence Strategy for Ireland
- AI Standards & Assurance Roadmap: Action under 'AI - Here for Good,' the National Artificial Intelligence Strategy for Ireland
- Artificial Intelligence: Friend or Foe? Summary and Public Policy Considerations
- Interim Guidelines for Use of AI in the Public Service
- Ireland's National AI Strategy: AI - Here for Good
- Bozza di linee guida per l’adozione di IA nella pubblica amministrazione
- Linee Guida per l’Introduzione dell’Intelligenza Artificiale nelle Istituzioni Scolastiche
- Piano Triennale per l’Informatica nella Pubblica Amministrazione
- Strategia Italiana per l’Intelligenza Artificiale 2024–2026
- National Artificial Intelligence Policy Recommendations
- Guide to Evaluation Perspectives on AI Safety
- Guide to Red Teaming Methodology on AI Safety
- Diplomat's Playbook on Artificial Intelligence—Shaping a Safe, Secure, Inclusive, and Trustworthy AI Future: Kenya's Strategic Leadership in AI Global Diplomacy
- Kenya Artificial Intelligence Strategy 2025-2030
- The National Guidelines on AI Governance & Ethics
- Recomendaciones para el Tratamiento de Datos Personales Derivado del Uso de la Inteligencia Artificial
- Cartea Albă cu Privire la Inteligența Artificială și Guvernanța Datelor
- White Book on Artificial Intelligence and Data Governance
- AI Act Guide
- AI Impact Assessment: The tool for a responsible AI project
- Call for input on prohibition on AI systems for emotion recognition in the areas of workplace or education institutions
- Accredited Employer Work Visa: Use of Adept for Automated Processing of Migrant Gateway
- Algorithm Assessment Report
- Algorithm impact assessment user guide: Algorithm Charter for Aotearoa New Zealand
- Artificial intelligence frameworks and regulation: An intelligence perspective - General of Intelligence and Security, August 2024
- Automated decision-making in MSD: Proposed legislative and policy framework
- Automated Decision Making Standard
- Discussion Paper: International Data Ethics Frameworks
- Government Use of Artificial Intelligence in New Zealand: Final Report on Phase 1 of the New Zealand Law Foundation's Artificial Intelligence and Law in New Zealand Project
- Initial advice on Generative Artificial Intelligence in the public service
- New Zealand Income Insurance: service model and automated decision making
- New Zealand's Strategy for Artificial Intelligence: Investing with confidence
- Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods
- NAIS National Artificial Intelligence Strategy
- Artificial Intelligence and Democratic Elections — International Experiences and National Recommendations
- National Strategy for Artificial Intelligence
- Artificial Intelligence Model Risk Management: Observations from a Thematic Review
- Guide for Using Generative AI in the Legal Sector
- National Artificial Intelligence Strategy: Advancing Our Smart Nation Journey
- The Singapore Consensus on Global AI Safety Research Priorities
- 2030 Digital Transformation Strategy for Slovakia: Strategy for transformation of Slovakia into a successful digital country
- Analýza a návrh možností výskumu, vývoja a aplikácie umelej inteligencie na Slovensku – Dielo č. 2: Manuál pre firmy na zavedenie umelej inteligencie
- Analýza a návrh možností výskumu, vývoja a aplikácie umelej inteligencie na Slovensku
- Preliminary position of the Slovak Republic on the “White Paper on Artificial Intelligence – A European approach to excellence and trust”
- Umelá inteligencia
- Umelá inteligencia vo vzdelávaní: Plán zodpovedného využívania AI vo vzdelávaní na Slovensku 2025–2027
- Akcijski načrt strategije Digitalna Slovenija 2030
- Zaveze za uporabo orodij generativne umetne inteligence, dostopnih na spletu
- Digital Public Services Strategy 2030
- Digitalna Slovenija 2030
- National Programme to Promote the Development and Use of Artificial Intelligence in the Republic of Slovenia by 2025 NpAI
- Register rabe UI
- Computer Applications Technology: Learner Guidelines for Practical Assessment Tasks, Grade 12, 2025
- South Africa's Artificial Intelligence Planning: Adoption of AI by Government
- AI Safety Institute of Korea
- Basic Act on the Promotion of Artificial Intelligence Development and Establishment of a Trust Framework
- 인공지능 발전과 신뢰 기반 조성 등에 관한 기본법
- 생성형 인공지능(AI) 개발·활용을 위한 개인정보 처리 안내서(안)
- Digital Switzerland Strategy 2025
- Artificial Intelligence Readiness Assessment Report
- Дорожня карта з регулювання штучного інтелекту в Україні: Bottom-Up Підхід
- Guidelines on the Responsible Use of Artificial Intelligence in the News Media
- AI and the Law: A Discussion Paper
- AI Safety Institute, Safety cases at AISI
- Artificial Intelligence Playbook for the UK Government
- Evaluation of the Cyber AI Hub programme | January 8, 2025
- Generative Artificial Intelligence in the Education System
- Global Coalition on Telecommunications: principles on AI adoption in the telecommunications industry
- Information Commissioner's Office, AI tools in recruitment
- Media literacy - 25
- Northern Ireland response to the AI Council AI Roadmap
- Parliamentary Office of Science and Technology
- The safe and effective use of AI in education: Leadership toolkit video transcripts
- Trusted third-party AI assurance roadmap
- US AISI and UK AISI Joint Pre-Deployment Test: Anthropic's Claude 3.5 Sonnet
- US AISI and UK AISI Joint Pre-Deployment Test: OpenAI o1
- Use of AI in Legislatures
- Bureau of Labor Statistics Report to the Committees on Appropriations of the House of Representatives and the Senate on Measuring the Effects of New Technologies on the American Workforce
- Incorporating AI impacts in BLS employment projections: occupational case studies
- H.R. 9720, AI Incident Reporting and Security Enhancement Act
- Artificial Intelligence in Health Care
- Artificial Intelligence and Machine Learning in Financial Services
- Artificial Intelligence: Background, Selected Issues, and Policy Considerations
- Highlights of the 2023 Executive Order on Artificial Intelligence for Congress
- Copyright and Artificial Intelligence Part 1 Digital Replicas
- Copyright and Artificial Intelligence Part 3 Generative AI Training
- Fiscal Year 2025-2026 AI Strategy
- Artificial intelligence
- Bureau of Industry and Security
- Department of Commerce Rescinds Biden-Era Artificial Intelligence Diffusion Rule, Strengthens Chip-Related Export Controls
- Framework for Artificial Intelligence Diffusion
- Evaluation of DeepSeek AI Models
- National Telecommunications and Information Administration
- AI System Documentation
- NTIA Artificial Intelligence Accountability Policy Report
- Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations updated - 2e2025
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile - 1
- NOAA Artificial Intelligence Strategy: Analytics for Next-Generation Earth Science
- Generative Artificial Intelligence and Open Data
- Outline: Proposed Zero Draft for a Standard on AI Testing, Evaluation, Verification, and Validation
- SP 800-53 Control Overlays for Securing AI Systems
- AI Data Security
- Content Credentials: Strengthening Multimedia Integrity in the Generative AI Era
- U.S. Department of Defense Responsible Artificial Intelligence Strategy and Implementation Pathway
- Inventory of U.S. Department of Education AI Use Cases
- Artificial Intelligence and Technology Office
- Strategic Plan for the Use of Artificial Intelligence in Health Human Services and Public Health Strategic Plan
- Acquisition and Use of Artificial Intelligence and Machine Learning Technologies by DHS Components - 06, August 8, 2023
- Artificial Intelligence and Autonomous Systems
- Artificial Intelligence Safety and Security Board
- Department of Homeland Security Artificial Intelligence Roadmap 2024
- DHS Has Taken Steps to Develop and Govern Artificial Intelligence, But More Action is Needed to Ensure Appropriate Use - 25-10, January 30, 2025
- DHS Playbook for Public Sector Generative Artificial Intelligence Deployment
- Roles and Responsibilities Framework for Artificial Intelligence in Critical Infrastructure
- The Department of Homeland Security Simplified Artificial Intelligence Use Case Inventory
- AI at DHS: A Deep Dive into our Use Case Inventory
- Artificial Intelligence Strategy for the U.S. Department of Justice
- Civil Rights Division, Artificial Intelligence and Civil Rights
- Shaping the Department's Artificial Intelligence Efforts 2021-2025
- Artificial Intelligence Use Case Inventory
- Validation of Employee Selection Procedures
- Artificial Intelligence
- AI Inventory 2024
- Enterprise Artificial Intelligence Strategy FY2024-FY-2025 Empowering Diplomacy through Responsible AI
- Interim Policy for AI Governance
- Building the Future: VA’s Strategy for Adopting High-Impact Artificial Intelligence to Improve Services for Veterans
- Fact Sheet Eliminating Barriers for Federal Artificial Intelligence Use and Procurement
- Winning the Race: America's AI Action Plan
- Roadmap for Artificial Intelligence Safety Assurance
- Advisory Bulletin AB 2013-07 Model Risk Management Guidance
- Privacy Policy
- Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations
- AI Guide for Government
- Highlights of GAO-21-519SP
- Artificial Intelligence: Generative AI Use and Management at Federal Agencies
- Artificial Intelligence: Use and Oversight in Financial Services GAO-25-107197
- Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products Draft Guidance
- Fraud and Improper Payments: Data Quality and a Skilled Workforce Are Essential for Unlocking the Benefits of Artificial Intelligence
- Generative AI's Environmental and Human Effects
- Veteran Suicide: VA Efforts to Identify Veterans at Risk through Analysis of Health Record Information
- NASA Framework for the Ethical Use of Artificial Intelligence - 20210012886, April 2021
- Potential Labor Market Impacts of Artificial Intelligence: An Empirical Analysis
- Letter to Congress Opposing AI Preemption Amendment
- Policy on the Use of Artificial Intelligence for NEH Grant Proposals
- AI in Financial Services Remarks at NFHA Responsible AI Symposium
- Artificial Intelligence Ethics Framework for the Intelligence Community v 1.0 as of June 2020
- Annual Threat Assessment of the U.S. Intelligence Community
- M-25-21 Memorandum for the Heads of Executive Departments and Agencies - Accelerating Federal Use of AI through Innovation, Governance, and Public Trust
- M-25-22 Memorandum for the Heads of Executive Departments and Agencies - Driving Efficient Acquisition of Artificial Intelligence in Government
- The Artificial Intelligence Classification Policy and Talent Acquisition Guidance - The AI in Government Act of 2020
- Investor Advisory Committee Meeting Agenda for Thursday
- Compliance Plan for OMB Memoranda M-24-10
- Bipartisan House Task Force Report on Artificial Intelligence
- Letter to Inflection AI re: AI Censorship
- Decoupling America’s Artificial Intelligence Capabilities from China Act
- Driving U.S. Innovation in Artificial Intelligence: A Roadmap for Artificial Intelligence Policy in the United States Senate
- Letter to DOJ Re FARA AI Violation
- Letter to Sundar Pichai concerning Google's decision to reverse its previous safety and ethical commitments on its development of AI products
- Generative AI Task Force Final Report
- Office of the Attorney General, California Attorney General's Legal Advisory on the Application of Existing California Laws to Artificial Intelligence
- California Privacy Protection Agency, Draft Risk Assessment and Automated Decisionmaking Technology Regulations
- The California Report on Frontier AI Policy
- Sonoma County Administrative Policy 9-6 Information Technology Artificial Intelligence Policy
- Report and Recommendations: Artificial Intelligence Impact Task Force
- State of Connecticut Judicial Branch JBAPPM Policy 1013 Artificial Intelligence Responsible Use Framework, Meaningful Guardrails + Workforce Empowerment and Education + Purposeful Use = Responsible AI Innovation
- State of Connecticut Policy AI-01 AI Responsible Use Framework, Meaningful Guardrails + Workforce Empowerment and Education + Purposeful Use = Responsible AI Innovation
- Provenance of Digital Content Florida HB 369 Bill Analysis
- Report on Miami-Dade County's Policy on Artificial Intelligence–Directive No. 231203 - Dade County, March 22, 2024
- Second Report on Miami-Dade County's Policy on Artificial Intelligence Directive No. 231203 - Dade County, April 8, 2025
- Illinois Supreme Court Policy on Artificial Intelligence
- State of Indiana Artificial Intelligence
- 080.101 AI/Gen AI Policy Version 1.1
- Artificial Intelligence Guidance Brief 2024
- Research Report No. 491 Executive Branch Use of Artificial Intelligence Technology
- Generative Artificial Intelligence Policy
- Enterprise Use and Development of Generative Artificial Intelligence Policy
- Nebraska Information Technology Commission 8-609 Artificial intelligence policy
- Legal Practice Preliminary Guidelines on the Use of Artificial Intelligence by New Jersey Lawyers
- Acceptable Use of Artificial Intelligence Technologies
- The New York City Artificial Intelligence Action Plan
- New York State Emerging Technology Advisory Board: Recommendations for making NY a leader in responsible AI
- New York State Artificial Intelligence Governance - S-50, April 2025
- AI Accelerator
- North Carolina State Government Responsible Use of Artificial Intelligence Framework
- South Carolina State Agencies Artificial Intelligence Strategy
- Artificial Intelligence Policy
- Lessons from Pennsyklvania's Generative AI Pilot with ChatGPT
- Artificial Intelligence and Generative AI Policy ISM 20
- Enterprise Artificial Intelligence policy 200-POL-007
- Artificial Intelligence Framework for Utah P-12 Education
- Policy Standards for the Utilization of Artificial Intelligence by the Commonwealth of Virginia
- City of Seattle Generative Artificial Intelligence Policy POL-209
- Washington Technology Solutions Reports & Documents
- Guidelines for Deployment of Generative AI
- Implementing risk assessments for high-risk AI systems
- Initial procurement guidelines for public sector procurement, deployment, and monitoring of Generative AI Technology
- Interim Guidelines for Purposeful and Responsible Use of Generative Artificial Intelligence
- Office of Privacy and Data Protection Performance Report
- Responsible AI in the Public Sector: How the Washington State Government Uses & Governs Artificial Intelligence
- State of Washington Generative Artificial Intelligence Report
- AI Guidance Resources
- Guidance for Wyoming School Districts on Developing Artificial Intelligence Use Policy
- ASEAN Guide on AI Governance and Ethics
- Democracy and the Rule of Law
- Discussion paper on Draft Recommendation on AI literacy
- European Audiovisual Observatory, IRIS, AI and the audiovisual sector: navigating the current legal landscape
- Guidelines on the Responsible Implementation of Artificial Intelligence Systems in Journalism
- On the Use of Artificial Intelligence in the Framework of the Syrian War
- Privacy and Data Protection Risks in Large Language Models
- Recommendation CM/Rec-2020-1 of the Committee of Ministers to member States on the human rights impacts of algorithmic systems
- The Framework Convention on Artificial Intelligence
- Explanatory Report to the Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law
- AI Act: Commission issues draft guidance and reporting template on serious AI incidents, and seeks stakeholders' feedback
- AI-driven Innovation in Medical Imaging: Focus on Lung Cancer and Cardiovascular Diseases
- Addressing AI risks in the workplace: Workers and algorithms
- Artificial Intelligence and Civil Liability: A European Perspective - General for Citizens' Rights, Justice and Institutional Affairs, July 2025
- Artificial intelligence and human rights: Using AI as a weapon of repression and its impact on human rights
- Analysis of the preliminary AI standardisation work plan in support of the AI Act
- Communication from the Commission, Artificial Intelligence for Europe
- Data Protection Certification Mechanisms: Study on Articles 42 and 43 of the Regulation 2016/679
- Data quality and artificial intelligence - mitigating bias and error to protect fundamental rights
- Ethical guidelines on the use of artificial intelligence and data in teaching and learning for Educators
- Ethics By Design and Ethics of Use Approaches for Artificial Intelligence
- Ethics Guidelines for Trustworthy AI - Level Expert Group on Artificial Intelligence, April 8, 2019
- European approach to artificial intelligence
- First Draft of the General-Purpose AI Code of Practice published, written by independent experts
- A Framework to Categorise Modified General-Purpose AI Models as New Models Based on Behavioural Changes
- Generative AI and the EUDPR. Orientations for ensuring data protection compliance when using Generative AI systems. Version 2
- Living Guidelines on the Responsible Use of Generative AI in Research
- Living repository to foster learning and exchange on AI literacy
- Living Repository of AI Literacy Practices
- Policy and Investment Recommendations for Trustworthy AI - Level Expert Group on Artificial Intelligence
- Procurement of AI Updated EU AI model contractual clauses
- Work in the Digital Era: How Technology is Transforming Work and Occupations
- Proposal for a directive on adapting non-contractual civil liability rules to artificial intelligence: Complementary impact assessment
- Proposal for a Regulation laying down harmonised rules on artificial intelligence
- The impact of the General Data Protection Regulation on artificial intelligence
- Roadmap for lawful and effective access to data for law enforcement
- Artificial intelligence act: Council and Parliament strike a deal on the first rules for AI in the world
- Council Conclusions on the Use of Artificial Intelligence in the Field of Justice
- AI Auditing documents
- Data Protection Authority of Belgium General Secretariat, Artificial Intelligence Systems and the GDPR: A Data Protection Perspective
- Generative AI and the EUDPR. First EDPS Orientations for ensuring data protection compliance when using Generative AI systems
- Opinion 28/2024 on certain data protection aspects related to the processing of personal data in the context of AI models
- Training curriculum on AI and data protection: Fundamentals of Secure AI Systems with Personal Data
- Analysis of EU AI Office stakeholder consultations: defining AI systems and prohibited applications
- Multi-Stakeholder Consultation for Commission Guidelines on the Application of the Definition of an AI System and the Prohibited AI Practices Established in the AI Act
- The changing DNA of serious and organised crime
- Guiding Principles for Automated Decision-Making in the EU
- Trustworthiness for AI in Defence: Developing Responsible, Ethical, and Trustworthy AI Systems for European Defence
- Algorithm Impact Assessment Toolkit
- OECD.AI Catalogue of Tools & Metrics for Trustworthy AI, Anekanta AI, Responsible AI Governance Framework for boards
- OECD Artificial Intelligence Papers
- No. 1, September 18, 2023, Initial policy considerations for generative artificial intelligence
- No. 2, October 17, 2023, Emerging trends in AI skill demand across 14 OECD countries
- No. 3, October 27, 2023, The state of implementation of the OECD AI Principles four years on
- No. 4, October 27, 2023, Stocktaking for the development of an AI incident definition
- No. 5, November 7, 2023, Common guideposts to promote interoperability in AI risk management
- No. 6, November 13, 2023, What technologies are at the core of AI?
- No. 7, November 24, 2023, Using AI to support people with disability in the labour market
- No. 8, March 5, 2024, Explanatory memorandum on the updated OECD definition of an AI system
- No. 9, December 15, 2023, Generative artificial intelligence in finance
- No. 10, January 19, 2024, Collective action for responsible AI in health
- No. 11, March 15, 2024, Using AI in the workplace
- No. 12, March 22, 2024, Generative AI for anti-corruption and integrity in government
- No. 13, April 10, 2024, Artificial intelligence and wage inequality
- No. 14, April 10, 2024, Artificial intelligence and the changing demand for skills in the labour market
- No. 15, April 16, 2024, The impact of Artificial Intelligence on productivity, distribution and growth
- No. 16, May 6, 2024, Defining AI incidents and related terms
- No. 17, May 30, 2024, Artificial intelligence and the changing demand for skills in Canada
- No. 18, May 24, 2024, Artificial intelligence, data and competition
- No. 19, June 13, 2024, A new dawn for public employment services
- No. 20, June 13, 2024, Governing with Artificial Intelligence
- No. 21, June 24, 2024, Using AI to manage minimum income benefits and unemployment assistance
- No. 22, June 26, 2024, AI, data governance and privacy
- No. 23, August 14, 2024, The potential impact of Artificial Intelligence on equity and inclusion in education
- No. 24, September 5, 2024, Regulatory approaches to Artificial Intelligence in finance
- No. 25, September 5, 2024, Measuring the demand for AI skills in the United Kingdom
- No. 26, October 31, 2024, Who will be the workers most affected by AI?
- No. 27, November 14, 2024, Assessing potential future artificial intelligence risks, benefits and policy imperatives
- No. 28, November 20, 2024, Artificial Intelligence and the health workforce
- No. 29, November 22, 2024, Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence
- No. 30, December 12, 2024, A Sectoral Taxonomy of AI Intensity
- No. 31, February 6, 2025, Algorithmic Management in the Workplace: New Evidence from an OECD Employer Survey
- No. 32, February 7, 2025, Steering AI's Future: Strategies for Anticipatory Governance
- No. 33, February 9, 2025, Intellectual Property Issues in Artificial Intelligence Trained on Scraped Data
- No. 34, February 28, 2025, Towards a Common Reporting Framework for AI Incidents
- No. 35, February 28, 2025, AI Skills and Capabilities in Canada
- OECD Legal Instruments, Recommendation of the Council on Artificial Intelligence, adopted May 22, 2019, amended May 3, 2024
- Open, Useful and Re-usable data Index: 2019
- Measuring the environmental impacts of artificial intelligence compute and applications
- #SAIFE Resource Hub: Spotlight on Artificial Intelligence and Freedom of Expression
- Artificial Intelligence and Disinformation: State-Aligned Information Operations and the Distortion of the Public Sphere
- Spotlight on Artificial Intelligence and Freedom of Expression: A Policy Manual
- AI in Precision Persuasion. Unveiling Tactics and Risks on Social Media
- "NATO-Mation": Strategies for Leading in the Age of Artificial Intelligence
- Summary of the NATO Artificial Intelligence Strategy
- An Artificial Intelligence Strategy for NATO
- Summary of NATO's revised Artificial Intelligence strategy
- Virtual Manipulation Brief 2025: From War and Fear to Confusion and Uncertainty
- Report of the Artificial Intelligence, Data Sovereignty, and Cybersecurity Task Force
- A Framework for Ethical AI at the United Nations, March 15, 2021
- A matter of choice: People and possibilities in the age of AI
- Casinos, cyber fraud, and trafficking in persons for forced criminality in Southeast Asia
- Governing AI for Humanity, Final Report
- High-Level Advisory Body on Artificial Intelligence - General's Envoy on Technology
- AI and education: guidance for policy-makers
- AI and the future of education: disruptions, dilemmas and directions
- Caribbean Artificial Intelligence Policy Roadmap
- Consultation paper on AI regulation: emerging approaches across the world
- Global AI Ethics and Governance Observatory
- Readiness assessment methodology: a tool of the Recommendation on the Ethics of Artificial Intelligence
- Recommendation on the Ethics of Artificial Intelligence
- Smarter, smaller, stronger: resource-efficient generative Al & the future of digital transformation
- Policy guidance on AI for children, Recommendations for building AI policies and systems that uphold child rights
- Principles for the ethical use of artificial intelligence in the United Nations system - 10-27
- Terms of Reference and Modalities for the Establishment and Functioning of the Independent International Scientific Panel on Artificial Intelligence and the Global Dialogue on Artificial Intelligence Governance
- Främja den offentliga förvaltningens förmåga att använda AI
- GDPR och AI
- Myndigheterna och AI
- Nationella AI-uppdraget: AI-guide, förtroendemodell m.m.
- Sweden AI Strategy Report
- Data Union Strategy: Unlocking Data for AI
- Autoridade Nacional de Proteção de Dados, Technology Radar – short version in English, no. 1: Generative Artificial Intelligence
- A Regulatory Framework for AI: Recommendations for PIPEDA Reform
- Directive on Automated Decision Making
- E-23 – Model Risk Management
- Artificial Intelligence: Friend or Foe? Summary and Public Policy Considerations
- Artificial Intelligence and Automation: Preserving Human Agency in a World of Automation
- Basic Act on the Promotion of Artificial Intelligence Development and Establishment of a Trust Framework
- U.S. Artificial Intelligence Safety Institute
- RAI Toolkit
- Office of Educational Technology
- Designing for Education with Artificial Intelligence: An Essential Guide for Developers
- Empowering Education Leaders: A Toolkit for Safe, Ethical, and Equitable AI Integration
- Artificial Intelligence Safety and Security Board
- AI at DHS: A Deep Dive into our Use Case Inventory
- Civil Rights Division, Artificial Intelligence and Civil Rights
- Artificial Intelligence
- AI Inventory 2024
- Framework to Advance AI Governance and Risk Management in National Security
- Principles of Artificial Intelligence Ethics for the Intelligence Community
- The Artificial Intelligence Classification Policy and Talent Acquisition Guidance - The AI in Government Act of 2020
- Artificial Intelligence Strategy
- Artificial Intelligence Governance Policy AI-GV-P1
- Sonoma County Administrative Policy 9-6 Information Technology Artificial Intelligence Policy
- State of Connecticut Policy AI-01 AI Responsible Use Framework, Meaningful Guardrails + Workforce Empowerment and Education + Purposeful Use = Responsible AI Innovation
- Nebraska Information Technology Commission 8-609 Artificial intelligence policy
- Acceptable Use of Artificial Intelligence Technologies
-
Community Frameworks and Guidance
- 8 Principles of Responsible ML
- A Brief Overview of AI Governance for Responsible Machine Learning Systems
- AI Verify Foundation
- Cataloguing LLM Evaluations, Draft for Discussion
- Generative AI: Implications for Trust and Governance
- AI Snake Oil
- AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
- AI Canon
- Anthropic's Responsible Scaling Policy
- AuditBoard: 5 AI Auditing Frameworks to Encourage Accountability
- Auditing machine learning algorithms: A white paper for public auditors
- Data Privacy FAQ
- Privacy Notice
- What is Data Governance?
- BIML Interactive Machine Learning Risk Framework
- November 7, 2023, The Executive Order on Safe, Secure, and Trustworthy AI: Decoding Biden’s AI Policy Roadmap
- October 2023, Decoding Intentions: Artificial Intelligence and Costly Signals
- August 11, 2023, Understanding AI Harms: An Overview
- August 1, 2023, Large Language Models (LLMs): An Explainer
- July 21, 2023, Making AI (more) Safe, Secure, and Transparent: Context and Research from CSET
- July 2023, Adding Structure to AI Harm: An Introduction to CSET's AI Harm Framework
- June 2023, The Inigo Montoya Problem for Trustworthy AI: The Use of Keywords in Policy and Research
- June 2023, A Matrix for Selecting Responsible AI Frameworks
- March 2023, Reducing the Risks of Artificial Intelligence for Military Decision Advantage
- February 2023, One Size Does Not Fit All: Assessment, Safety, and Trust for the Diverse Range of AI Products, Tools, Services, and Resources
- January 2023, Forecasting Potential Misuses of Language Models for Disinformation Campaigns—and How to Reduce Risk
- October 2022, A Common Language for Responsible AI: Evolving and Defining DOD Terms for Implementation
- December 2021, AI and the Future of Disinformation Campaigns: Part 1: The RICHDATA Framework
- December 2021, AI and the Future of Disinformation Campaigns: Part 2: A Threat Model
- July 2021, AI Accidents: An Emerging Threat: What Could Happen and What to Do
- May 2021, Truth, Lies, and Automation: How Language Models Could Change Disinformation
- March 2021, Key Concepts in AI Safety: An Overview
- February 2021, Trusted Partners: Human-Machine Teaming and the Future of Military AI
- AI Audit
- Internal auditor's AI safety checklist
- DAIR Prompt Engineering Guide
- The Data Cards Playbook
- Data Provenance Explorer
- AI Red-Teaming Is Not a One-Stop Solution to AI Harms: Recommendations for Using Red-Teaming for AI Accountability
- Dealing with Bias and Fairness in AI/ML/Data Science Systems
- Debugging Machine Learning Models
- Decision Points in AI Governance
- Evaluating LLMs is a minefield
- Extracting Training Data from ChatGPT
- FATML Principles and Best Practices
- ForHumanity Body of Knowledge
- The Foundation Model Transparency Index
- From Principles to Practice: An interdisciplinary framework to operationalise AI ethics
- Frontier Model Forum: What is Red Teaming?
- Gage Repeatability and Reproducibility
- Georgetown University Library's Artificial Intelligence (Generative) Resources
- Data governance in the cloud - part 1 - People and processes
- Data Governance in the Cloud - part 2 - Tools
- Evaluating social and ethical risks from generative AI
- Generative AI Prohibited Use Policy
- Principles and best practices for data governance in the cloud
- Responsible AI Framework
- H2O.ai Algorithms - tutorials?style=social)
- How to Perform an AI Audit for UK Organisations
- Hogan Lovells, The AI Act is coming: EU reaches political agreement on comprehensive regulation of artificial intelligence
- The Landscape of ML Documentation Tools
- IAPP EU AI Act Cheat Sheet
- A checklist for auditing AI systems
- P3119 Standard for the Procurement of Artificial Intelligence and Automated Decision Systems
- Std 1012-1998 Standard for Software Verification and Validation
- Independent Audit of AI Systems
- Identifying and Eliminating CSAM in Generative ML Training Data and Models
- Identifying and Overcoming Common Data Mining Mistakes
- First of its kind Generative AI Evaluation Sandbox for Trusted AI by AI Verify Foundation and IMDA
- Institute of Internal Auditors: Artificial Intelligence Auditing Framework, Practical Applications, Part A, Special Edition
- Auditing Artificial Intelligence
- Auditing Guidelines for Artificial Intelligence
- Capability Maturity Model Integration Resources
- Large language models, explained with a minimum of math and jargon
- Information System Contingency Planning Guidance
- Llama 2 Responsible Use Guide
- LLM Visualization
- Machine Learning Attack_Cheat_Sheet
- Machine Learning Quick Reference: Algorithms
- Machine Learning Quick Reference: Best Practices
- Towards Traceability in Data Ecosystems using a Bill of Materials Model
- Microsoft AI Red Team building future of safer AI
- Microsoft Responsible AI Standard, v2
- NewsGuard AI Tracking Center
- Open Sourcing Highly Capable Foundation Models
- OpenAI Red Teaming Network
- Organization and Training of a Cyber Security Team
- Our Data Our Selves, Data Use Policy
- ABOUT ML Reference Document
- Responsible Practices for Synthetic Media: A Framework for Collective Action
- Real-World Strategies for Model Debugging
- RecoSense: Phases of an AI Data Audit – Assessing Opportunity in the Enterprise
- Red Teaming of Advanced Information Assurance Concepts
- Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
- Robust ML
- Safe and Reliable Machine Learning
- SHRM Generative Artificial Intelligence (AI) Chatbot Usage Policy
- Responsible AI at Stanford: Enabling innovation through AI best practices
- The Rise of Generative AI and the Coming Era of Social Media Manipulation 3.0: Next-Generation Chinese Astroturfing and Coping with Ubiquitous AI
- Taskade: AI Audit PBC Request Checklist Template
- TechTarget: 9 questions to ask when auditing your AI systems
- Troubleshooting Deep Neural Networks
- Twitter Algorithmic Bias Bounty
- Unite.AI: How to perform an AI Audit in 2023
- University of California, Berkeley, Center for Long-Term Cybersecurity, A Taxonomy of Trustworthiness for Artificial Intelligence
- University of California, Berkeley, Information Security Office, How to Write an Effective Website Privacy Statement
- Warning Signs: The Future of Privacy and Security in an Age of Machine Learning
- When Not to Trust Your Explanations
- You Created A Machine Learning Application Now Make Sure It's Secure
- Artificial Intelligence in the Securities Industry
- CDAO frameworks, guidance, and best practices for AI test & evaluation
- Lakera AI's Gandalf
- Organization and Training of a Cyber Security Team
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- PAIR Explorables: Datasets Have Worldviews
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- Know Your Data
- System cards
- Real-World Strategies for Model Debugging
- University of Washington Tech Policy Lab, Data Statements
- coolaj86 / Chat GPT "DAN" (and other "Jailbreaks")
- Jay Alammar, Interfaces for Explaining Transformer Language Models
- CSET Publications
- ISO/IEC 42001:2023, Information technology — Artificial intelligence — Management system
- Azure AI Content Safety
- Harm categories in Azure AI Content Safety
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- Berryville Institute of Machine Learning, Architectural Risk Analysis of Large Language Models (requires free account login)
- Fairly's Global AI Regulations Map - ai-regulations-map?style=social)
- How to implement LLM guardrails
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- CivAI, GenAI Toolkit for the NIST AI Risk Management Framework: Thinking Through the Risks of a GenAI Chatbot
- Real-World Strategies for Model Debugging
- ACL 2024 Tutorial: Vulnerabilities of Large Language Models to Adversarial Attacks
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- Trustible, Is It AI? How different laws & frameworks define AI
- @dotey on X/Twitter exploring GPT prompt security and prevention measures
- Exploiting Novel GPT-4 APIs
- Learn Prompting, Prompt Hacking
- MiesnerJacob / learn-prompting, Prompt Hacking - prompting?style=social)
- r/ChatGPTJailbreak
- Y Combinator, ChatGPT Grandma Exploit
- Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
- Real-World Strategies for Model Debugging
- AI Incident Collection: An Observational Study of the Great AI Experiment
- Repurposing the Wheel: Lessons for AI Standards
- Translating AI Risk Management Into Practice
- Coalition for Content Provenance and Authenticity
- HackerOne Blog
- Building an early warning system for LLM-aided biological threat creation
- Guidance for Safe Foundation Model Deployment: A Framework for Collective Action
- Framework for Identifying Highly Consequential AI Use Cases
- The Complete Guide to Crowdsourced Security Testing, Government Edition
- Future of Privacy Forum, The Spectrum of Artificial Intelligence
- CSET, What Does AI-Red Teaming Actually Mean?
- HackerOne, An Emerging Playbook for AI Red Teaming with HackerOne
- Jailbreaking Black Box Large Language Models in Twenty Queries
- How do I cite generative AI in MLA style?
- On Risk Assessment and Mitigation for Algorithmic Systems
- Open Source Audit Tooling (OAT) Landscape
- Analyzing Harms from AI-Generated Images and Safeguarding Online Authenticity
- Real-World Strategies for Model Debugging
- Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
- Real-World Strategies for Model Debugging
- Oliver Patel's Cheat Sheets
- 10 Key Pillars for Enterprise AI Governance
- AI Governance in 2023
- China AI Law Cheat Sheet
- EU AI Act Cheat Sheet
- Governance Audit, Model Audit, and Application Audit
- Gulf Countries AI Policy Cheat Sheet
- Singapore AI Policy Cheat Sheet
- UK AI Policy Cheat Sheet
- developer mode fixed
- Real-World Strategies for Model Debugging
- An Overview of Catastrophic AI Risks
- Real-World Strategies for Model Debugging
- Center for AI and Digital Policy Reports
- Ethics for people who work in tech
- Towards Effective Governance of Foundation Models and Generative AI
- Transformed by AI: How Generative Artificial Intelligence Could Affect Work in the UK—And How to Manage It
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- Boston University AI Task Force Report on Generative AI in Education and Research
- Real-World Strategies for Model Debugging
- Tech Policy Press - Artificial Intelligence
- Phil Lee, AI Act: Difference between AI systems and AI models
- Phil Lee, AI Act: Meet the regulators! (Arts 30, 55b, 56 and 59)
- Phil Lee, How the AI Act applies to integrated generative AI
- Phil Lee, Overview of AI Act requirements for deployers of high risk AI systems
- Phil Lee, Overview of AI Act requirements for providers of high risk AI systems
- Attention Is All You Need
- Real-World Strategies for Model Debugging
- Real-World Strategies for Model Debugging
- 10 Key Pillars for Enterprise AI Governance
- AI Governance in 2023
- China AI Law Cheat Sheet
- EU AI Act Cheat Sheet
- Governance Audit, Model Audit, and Application Audit
- Gulf Countries AI Policy Cheat Sheet
- Singapore AI Policy Cheat Sheet
- UK AI Policy Cheat Sheet
- Real-World Strategies for Model Debugging
- How Can We Tackle AI-Fueled Misinformation and Disinformation in Public Health?
- Model Transparency Ratings
- Perspectives on Issues in AI Governance
- AI-Relevant Regulatory Precedents: A Systematic Search Across All Federal Agencies
- GDPR and Generative AI: A Guide for Public Sector Organizations
- Real-World Strategies for Model Debugging
- AppliedAI Institute, Navigating the EU AI Act: A Process Map for making AI Systems available
- Canada AI Law & Policy Cheat Sheet
- India AI Policy Cheat Sheet
- LLM Agents can Autonomously Exploit One-day Vulnerabilities
- No, LLM Agents can not Autonomously Exploit One-day Vulnerabilities
- Humane Intelligence, SeedAI, and DEFCON AI Village, Generative AI Red Teaming Challenge: Transparency Report 2024
- AI Governance Needs Sociotechnical Expertise: Why the Humanities and Social Sciences Are Critical to Government Efforts
- Applying Sociotechnical Approaches to AI Governance in Practice
- IBM, The CEO's Guide to Generative AI
- GraphRAG: Unlocking LLM discovery on narrative private data
- Real-World Strategies for Model Debugging
- BCG Robotaxonomy
- Purpose and Means AI Explainer Series - issue #4 - Navigating the EU AI Act
- Phil Lee, AI Act: Difference between AI systems and AI models
- Phil Lee, AI Act: Meet the regulators! (Arts 30, 55b, 56 and 59)
- Phil Lee, How the AI Act applies to integrated generative AI
- Phil Lee, Overview of AI Act requirements for deployers of high risk AI systems
- Phil Lee, Overview of AI Act requirements for providers of high risk AI systems
- Deloitte, Trust in the age of automation and Generative AI
- AI in the Public Service: From Principles to Practice
- Casey Flores, AIGP Study Guide
- Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
- Center for Security and Emerging Technology (CSET), High Level Comparison of Legislative Perspectives on Artificial Intelligence US vs. EU
- Future of Privacy Forum, EU AI Act: A Comprehensive Implementation & Compliance Timeline
- Canada AI Law & Policy Cheat Sheet
- Definitions, Scope & Applicability EU AI Act Cheat Sheet Series, Part 1
- Model Governance Framework for Generative AI
- World Privacy Forum, Risky Analysis: Assessing and Improving AI Governance Tools
- IAPP, EU AI Act Compliance Matrix
- IAPP, EU AI Act Compliance Matrix - At a Glance
- EU AI Act Cheat Sheet Series 2, Prohibited AI Systems
- Berkeley Center for Long-Term Cybersecurity (CLTC), https://cltc.berkeley.edu/publication/benchmark-early-and-red-team-often-a-framework-for-assessing-and-managing-dual-use-hazards-of-ai-foundation-models/
- BCG Robotaxonomy
- India AI Policy Cheat Sheet
- Ada Lovelace Institute, Code and Conduct: How to Create Third-Party Auditing Regimes for AI Systems
- Ravit Dotan's Projects
- AI Policy
- Trustible, Enhancing the Effectiveness of AI Governance Committees
- EU AI Act Cheat Sheet Series 1, Definitions, Scope & Applicability
- EU AI Act Cheat Sheet Series 3, High-Risk AI Systems
- India AI Policy Cheat Sheet
- AI Ethics and Governance in Practice
- EU AI Act Cheat Sheet Series 4, Requirements for Providers
- Purpose and Means AI Explainer Series - issue #4 - Navigating the EU AI Act
- World Economic Forum, Responsible AI Playbook for Investors
- A-LIGN, ISO 42001 Requirement, NIST SP 800-218A Task, Recommendations and Considerations
- Future of Privacy Forum, EU AI Act: A Comprehensive Implementation & Compliance Timeline
- Responsible Data Stewardship in Practice
- Digital Policy Alert, The Anatomy of AI Rules: A systematic comparison of AI rules across the globe
- Federation of American Scientists, A NIST Foundation To Support The Agency’s AI Mandate
- Advancing AI responsibly
- Open Data Institute, Understanding data governance in AI: Mapping governance
- Center for Security and Emerging Technology (CSET), High Level Comparison of Legislative Perspectives on Artificial Intelligence US vs. EU
- Definitions, Scope & Applicability EU AI Act Cheat Sheet Series, Part 1
- EU AI Act Cheat Sheet Series 5, Requirements for Deployers
- Backpack Language Models
- The Remarkable Robustness of LLMs: Stages of Inference?
- Neuronpedia
- EU AI Act Cheat Sheet Series 2, Prohibited AI Systems
- European Data Protection Board (EDPB), Checklist for AI Auditing
- Instruction finetuning an LLM from scratch
- EU AI Act Cheat Sheet Series 1, Definitions, Scope & Applicability
- EU AI Act Cheat Sheet Series 3, High-Risk AI Systems
- EU AI Act Cheat Sheet Series 6, General-Purpose AI Models
- EU AI Act Cheat Sheet Series 7, Compliance & Conformity Assessment
- India AI Policy Cheat Sheet
- Pivot to AI
- Jay Alammar, Finding the Words to Say: Hidden State Visualizations for Language Models
- EU AI Act Cheat Sheet Series 4, Requirements for Providers
- ChatGPT_system_prompt
- DAIR Prompt Engineering Guide GitHub - ai/Prompt-Engineering-Guide?style=social)
- model-cards-and-datasheets - cards-and-datasheets?style=social)
- Adversarial ML Threat Matrix
- Evals
- Azure's PyRIT
- In-The-Wild Jailbreak Prompts on LLMs
- 0xk1h0 / ChatGPT "DAN" (and other "Jailbreaks")
- Membership Inference Attacks and Defenses on Machine Learning Models Literature - inference-machine-learning-literature?style=social)
- Acceptable Use Policies for Foundation Models
- Access Now, Regulatory Mapping on Artificial Intelligence in Latin America: Regional AI Public Policy Report
- CSET's Harm Taxonomy for the AI Incident Database - cset/CSET-AIID-harm-taxonomy?style=social)
- Demos, AI – Trustworthy By Design: How to build trust in AI systems, the institutions that create them and the communities that use them
- Global AI Governance Law and Policy: Canada, EU, Singapore, UK and US
- LC Labs AI Planning Framework - ai-framework?style=social), Library of Congress
- Manifest MLBOM Wiki
- Sample AI Incident Response Checklist
- A-LIGN, ISO 42001 Requirement, NIST SP 800-218A Task, Recommendations and Considerations
- What Access Protections Do AI Companies Provide for Independent Safety Research?
- LLM Security & Privacy - sp?style=social)
- EU AI Act Cheat Sheet Series 5, Requirements for Deployers
- A Brief Overview of AI Governance for Responsible Machine Learning Systems
- AI Model Registries: A Foundational Tool for AI Governance
- AI Standards Hub
- Language Model Risk Cards: Starter Set
- Deepfake Pornography Goes to Washington: Measuring the Prevalence of AI-Generated Non-Consensual Intimate Imagery Targeting Congress
- University of California, Los Angeles, Artificial Intelligence Tools and Academic Use
- Casey Flores, AIGP Study Guide
- AI Accidents: An Emerging Threat: What Could Happen and What to Do, CSET Policy Brief, July 2021
- Chinese Critiques of Large Language Models: Finding the Path to General Intelligence
- An In-Depth Guide To Help You Start Auditing Your AI Models
- Center for Countering Digital Hate (CCDH), YouTube's Anorexia Algorithm: How YouTube Recommends Eating Disorders Videos to Young Girls
- AI Policy & Governance
- Assessing AI: Surveying the Spectrum of Approaches to Understanding and Auditing AI Systems
- In Deep Trouble: Surfacing Tech-Powered Sexual Harassment in K-12 Schools
- Centre for International Governance Innovation Publications
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of Developing, Implementing, and Using Advanced Warehouse Technologies: Top-Down Principles Versus The Guidance Ethics Approach
- Guidelines on the Application of the Definition of an AI System in the AI Act: ELI Proposal for a Three-Factor Approach
- Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey
- Generative AI Vendor Risk Assessment Guide - ISAC, February 2024,
- Best Practice Tools: Examples supporting responsible AI maturity
- The AI Act is coming: EU reaches political agreement on comprehensive regulation of artificial intelligence
- AI ethics in action: An enterprise guide to progressing trustworthy AI
- Design for AI
- Principles and Practices for Building More Trustworthy AI, October 6, 2021
- A Flexible Maturity Model for AI Governance Based on the NIST AI Risk Management Framework
- The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, General Principles
- An Overview of Artificial Intelligence Ethics
- ITI's AI Security Policy Principles
- International Bar Association and the Center for AI and Digital Policy, The Future Is Now: Artificial Intelligence and the Legal Profession
- Key questions for the International Network of AI Safety Institutes
- Mapping Technical Safety Research at AI Companies: A literature review and incentives analysis
- Understanding the First Wave of AI Safety Institutes: Characteristics, Functions, and Challenges
- The Implications of Artificial Intelligence in Cybersecurity: Shifting the Offense-Defense Balance
- Institute of Internal Auditors
- Guidelines for AI in parliaments - Parliamentary Union, December 2024
- Gen-AI: Artificial Intelligence and the Future of Work
- Implementing the AI Act in Belgium: Scope of Application and Authorities
- Towards Traceability in Data Ecosystems using a Bill of Materials Model
- AI Assurance: A Repeatable Process for Assuring AI-enabled Systems
- A Digital Pandemic: Uncovering the Role of 'Yahoo Boys' in the Surge of Social Media-Enabled Financial Sextortion Targeting Minors
- Guide for Preparing and Responding to Deepfake Events: From the OWASP Top 10 for LLM Applications Team
- Mitigating the risk of generative AI models creating Child Sexual Abuse Materials: An analysis by child safety nonprofit Thorn
- PwC's Responsible AI
- US Tort Liability for Large-Scale Artificial Intelligence Damages, A Primer for Developers and Policymakers
- Safe and Reliable Machine Learning
- Assessing the Implementation of Federal AI Leadership and Compliance Mandates - Centered Artificial Intelligence (HAI)
- Open Problems in Technical AI Governance: A repository of open problems in technical AI governance
- The Ethics of AI Ethics: An Evaluation of Guidelines
- International AI Safety Report: The International Scientific Report on the Safety of Advanced AI
- AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources
- The AI Act between Digital and Sectoral Regulations
- US Open-Source AI Governance: Balancing Ideological and Geopolitical Considerations with China Competition
- 2024 State of the AI Regulatory Landscape
- Artificial Intelligence Impact Assessment
- The Ethics of AI Ethics: An Evaluation of Guidelines
- How Microsoft names threat actors
- Forging Global Cooperation on AI Risks: Cyber Policy as a Governance Blueprint
- AI Inventories: Practical Challenges for Organizational Risk Management
- Recommendations for the Independent International Scientific Panel on AI and the Global Dialogue on AI Governance
- Toward an evaluation science for generative AI systems
- The Ethics of AI Ethics: An Evaluation of Guidelines
- Large language models explained with a minimum of math and jargon
- AI Inventories: Practical Challenges for Organizational Risk Management
- Llama 2 Responsible Use Guide
- Navigating the AI Frontier: A Primer on the Evolution and Impact of AI Agents
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- AI-Generated Disinformation in Europe and Africa: Use Cases, Solutions and Transnational Learning
- Character Flaws: School Shooters, Anorexia Coaches, and Sexualized Minors: A Look at Harmful Character Chatbots and the Communities That Build Them
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- leondz / garak
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- AI Governance Alliance Briefing Paper Series
- AI in Africa
- AI Liability Along the Value Chain
- Putting Explainable AI to the Test: A Critical Look at AI Evaluation Approaches
- Children & AI Design Code: A Protocol for the development and use of AI systems that impact children
- EU AI Act – Provider Only: Certification Scheme v1.5
- Governing Artificial Intelligence From Ethical Principles Toward Organizational AI Governance Practices
- Institute for AI Policy and Strategy
- AI Agent Governance: A Field Guide
- Just Security's Artificial Intelligence Archive
- Navigating AI Compliance Part 1 Tracing Failure Patterns in History
- Navigating AI Compliance Part 2 Risk Mitigation Strategies for Safeguarding Against Future Failures
- The Ethics of AI Ethics: An Evaluation of Guidelines
- Chain-of-thought Faithfulness
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- Responsible AI practices
- The Ethics of AI Ethics: An Evaluation of Guidelines
- AI Decision-Making and the Courts: A guide for Judges, Tribunal Members, and Court Administrators
- AI Ethics and Governance in Practice: AI Safety in Practice
- AI Safety in Practice
- CEN-CENELEC JTC21 AI Standards: Complete Detailed Overview
- 2025 Responsible AI Transparency Report: How we build, support our customers, and grow
- Taxonomy of Failure Mode in Agentic AI Systems
- Multi-Agent Risks from Advanced AI
- Disrupting malicious uses of AI: June 2025
- OWASP AI Testing Guide
- Real People in Fake Porn: How a Federal Right of Publicity Could Assist in the Regulation of Deepfake Pornography
- Risk Taxonomy and Thresholds for Frontier AI Frameworks
- Risk Tiers: Towards a Gold Standard for Advanced AI
- Adverse Event Reporting for AI: Developing the Information Infrastructure Government Needs to Learn and Act
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- Artificial Intelligence Controls Matrix Bundle
- The Ethics of AI Ethics: An Evaluation of Guidelines
- AI Model Risk Management Framework
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- AI Governance and the EU's Strategic Role in 2025
- Estimating the usage and utility of LLMs in the US general public
- Mapping AI Risk Mitigations: Evidence Scan and Draft Mitigation Taxonomy
- Strengthening Emergency Preparedness and Response for AI Loss of Control Incidents
- The Ethics of AI Ethics: An Evaluation of Guidelines
- AuditBoard: 5 AI Auditing Frameworks to Encourage Accountability
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- Ahead of the Curve: Governing AI Agents Under the EU AI Act - Polet, The Future Society, June 2025
- AI Act Governance: Best Practices for Implementing the EU AI Act
- AI alignment vs AI ethical treatment: Ten challenges
- AI Ethics & Governance 2025: A Framework for Malaysia's Tech Industry
- AI-Generated Algorithmic Virality
- AI Safety Governance, the Southeast Asian Way
- AI Sustainability Outlook: The Challenges, Potential, and Path Forward
- AI Won't Replace the General: Algorithms, Decision-making and Battlefield Command
- Artificial Intelligence in Africa: Challenges and Opportunities
- Artificial Intelligence Tools Versus Practice in Conflict Prediction: The Case of Mali
- Doing AI Differently: Rethinking the foundations of AI via the humanities
- Emotional Manipulation by AI Companions
- EU AI Act Handbook
- Evidence of CCP Censorship, Propaganda in U.S. LLM Responses
- Explainable AI in Finance: Addressing the Needs of Diverse Stakeholders - Ann Wilson, CFA Institute, Research & Policy Center, August 2025
- Fake Friend: How ChatGPT betrays vulnerable teens by encouraging dangerous behavior
- Generative AI: A New Threat for Online Child Sexual Exploitation and Abuse
- Guide for Australian Business: Understanding 42001
- Human-Calibrated Automated Testing and Validation of Generative Language Models: An Overview
- Key Considerations When Using Artificial Intelligence in the Public Sector
- Learning from other domains to advance AI evaluation and testing
- Opportunities to Strengthen U.S. Biosecurity from AI-Enabled Bioterrorism: What Policymakers Should Know
- Raising Standards: Data and Artificial Intelligence in Southeast Asia
- Regulating Under Uncertainty: Governance Options for Generative AI
- Responsible Enterprise AI in the Agentic Era
- State of Agentic AI Security and Governance: OWASP Gen AI Security Project Agentic Security Initiative
- State of AI Safety in China
- Summary Report: Workshop on the Geopolitics of Critical Minerals and the AI Supply Chain
- Synthetic Data: The New Data Frontier
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- AI Governance: A Framework for Responsible and Compliant Artificial Intelligence
- AI Governance InternationaL Evaluation AGILE Index 2025
- Countries With Draft AI Legislation or Frameworks
- A Guide to AI in Schools: Perspectives for the Perplexed
- Sovereign AI and Sustainable Computation for Indigenous Communities
- The Ethics of AI Ethics: An Evaluation of Guidelines
- The Ethics of AI Ethics: An Evaluation of Guidelines
- PwC's Responsible AI
- The Ethics of AI Ethics: An Evaluation of Guidelines
- Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph - latent--explorer-red)](https://github.com/Ipazia-AI/latent-explorer)
- Agentic AI: Fostering Responsible and Beneficial Development and Adoption
- AI for a Planet Under Pressure
- Ethical and social risks of harm from Language Models
- Responsible AI practices
- Governing Artificial Intelligence From Ethical Principles Toward Organizational AI Governance Practices
- How People Around the World View AI
- International AI Safety Report
- ISO policy brief: Harnessing international standards for responsible AI development and governance
- News Integrity in AI Assistants: An international PSM study
- Technology Trends Outlook 2025
- The Ethics of AI Ethics: An Evaluation of Guidelines
- Troubleshooting Deep Neural Networks
- What Are High-Risk AI Systems Within the Meaning of the EU’s AI Act, and What Requirements Apply to Them?
- Who Should Develop Which AI Evaluations?
- Why We Need to Know More: Exploring the State of AI Incident Documentation Practices
- AI Governance on the Ground: Canada’s Algorithmic Impact Assessment Process and Algorithm has evolved
- AI Value Alignment: Guiding Artificial Intelligence Towards Shared Human Goals
- Risky Analysis: Assessing and Improving AI Governance Tools
- Worldwide AI Ethics: A Review of 200 Guidelines and Recommendations for AI Governance
- AI Risk-Management Standards Profile for General-Purpose AI and Foundation Models
- Intolerable Risk Threshold Recommendations for Artificial Intelligence: Key Principles, Considerations, and Case Studies to Inform Frontier AI Safety Frameworks for Industry and Government
- Foundation Model Development Cheatsheet
- EU AI Act: A Comprehensive Implementation & Compliance Timeline
- Generative AI framework and Generative AI value tree modelling diagram
- Global Index for AI Safety: AGILE Index on Global AI Safety Readiness Feb 2025
- Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations
- GenAI Red Teaming Guide: A Practical Approach to Evaluating AI Vulnerabilities
- Red Teaming for GenAI Harms: Revealing the Risks and Rewards for Online Safety
- A Safe Harbor for AI Evaluation and Red Teaming
- Tracing the thoughts of a large language model
- Columbia Business School, Generative AI Policy
- Columbia University, Considerations for AI Tools in the Classroom
- Columbia University, Generative AI Policy
- Georgetown University, Artificial Intelligence and Homework Support Policies
- Georgetown University, Teaching with AI
- George Washington University, Faculty Resources: Generative AI
- George Washington University, Guidelines for Using Generative Artificial Intelligence at the George Washington University April 2023
- George Washington University, Guidelines for Using Generative Artificial Intelligence in Connection with Academic Work
- Harvard Business School, 2.1.2 Using ChatGPT & Artificial Intelligence Tools
- Harvard Graduate School of Education, HGSE AI Policy
- Harvard University, Guidelines for Using ChatGPT and other Generative AI tools at Harvard
- Massachusetts Institute of Technology, Guidance for use of Generative AI tools
- Massachusetts Institute of Technology, Generative AI & Your Course
- Stanford Graduate School of Business, Course Policies on Generative AI Use
- Stanford University, Artificial Intelligence Teaching Guide
- Stanford University, Creating your course policy on AI
- Stanford University, Generative AI Policy Guidance
- University of California, AI Governance and Transparency
- University of California, Applicable Law and UC Policy
- University of California, Legal Alert: Artificial Intelligence Tools
- University of California, Berkeley, AI at UC Berkeley
- University of California, Irvine, Generative AI for Teaching and Learning
- University of California, Irvine, Statement on Generative AI Detection
- University of California, Los Angeles, ChatGPT and AI Resources
- University of California, Los Angeles, Generative AI
- University of California, Los Angeles, Teaching Guidance for ChatGPT and Related AI Developments
- University of Notre Dame, AI Recommendations for Instructors
- University of Notre Dame, AI@ND Policies and Guidelines
- University of Notre Dame, Generative AI Policy for Students
- University of Southern California, Using Generative AI in Research
- University of Washington, AI+Teaching
- University of Washington, AI+Teaching, Sample syllabus statements regarding student use of artificial intelligence
- Yale University, AI at Yale
- Yale University, AI Guidance for Teachers
- Yale University, Yale University AI guidelines for staff
- Yale University, Guidelines for the Use of Generative AI Tools
- Architectural Risk Analysis of Large Language Models
- The Ethics of AI Ethics: An Evaluation of Guidelines
- Artificial Intelligence Harm and Human Rights: A High Level Exploration of the Interaction of AI Harms
- The Ethics of AI Ethics: An Evaluation of Guidelines
- Distill
- People + AI Guidebook
-
Conferences and Workshops
- AAAI Conference on Artificial Intelligence
- ACM FAccT (Fairness, Accountability, and Transparency)
- FAT/ML (Fairness, Accountability, and Transparency in Machine Learning)
- AIES (AAAI/ACM Conference on AI, Ethics, and Society)
- Black in AI
- Computer Vision and Pattern Recognition (CVPR)
- International Conference on Machine Learning (ICML)
- 2nd ICML Workshop on Human in the Loop Learning (HILL)
- 5th ICML Workshop on Human Interpretability in Machine Learning (WHI)
- Challenges in Deploying and Monitoring Machine Learning Systems
- Economics of privacy and data labor
- Federated Learning for User Privacy and Data Confidentiality
- Healthcare Systems, Population Health, and the Role of Health-tech
- Law & Machine Learning
- ML Interpretability for Scientific Discovery
- MLRetrospectives: A Venue for Self-Reflection in ML Research
- Participatory Approaches to Machine Learning
- XXAI: Extending Explainable AI Beyond Deep Models and Classifiers
- Human-AI Collaboration in Sequential Decision-Making
- Machine Learning for Data: Automated Creation, Privacy, Bias
- ICML Workshop on Algorithmic Recourse
- ICML Workshop on Human in the Loop Learning (HILL)
- ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
- Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ITR3)
- International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021 (FL-ICML'21)
- Interpretable Machine Learning in Healthcare
- Self-Supervised Learning for Reasoning and Perception
- The Neglected Assumptions In Causal Inference
- Theory and Practice of Differential Privacy
- Uncertainty and Robustness in Deep Learning
- Workshop on Computational Approaches to Mental Health @ ICML 2021
- Workshop on Distribution-Free Uncertainty Quantification
- Workshop on Socially Responsible Machine Learning
- 1st ICML 2022 Workshop on Safe Learning for Autonomous Driving (SL4AD)
- 2nd Workshop on Interpretable Machine Learning in Healthcare (IMLH)
- DataPerf: Benchmarking Data for Data-Centric AI
- Disinformation Countermeasures and Machine Learning (DisCoML)
- Responsible Decision Making in Dynamic Environments
- Spurious correlations, Invariance, and Stability (SCIS)
- The 1st Workshop on Healthcare AI and COVID-19
- Theory and Practice of Differential Privacy
- Workshop on Human-Machine Collaboration and Teaming
- 2nd ICML Workshop on New Frontiers in Adversarial Machine Learning
- 2nd Workshop on Formal Verification of Machine Learning
- 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)
- Challenges in Deployable Generative AI
- “Could it have been different?” Counterfactuals in Minds and Machines
- Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities
- Generative AI and Law (GenLaw)
- Interactive Learning with Implicit Human Feedback
- Neural Conversational AI Workshop - What’s left to TEACH (Trustworthy, Enhanced, Adaptable, Capable and Human-centric) chatbots?
- The Second Workshop on Spurious Correlations, Invariance and Stability
- Knowledge, Discovery, and Data Mining (KDD)
- 2nd ACM SIGKDD Workshop on Ethical Artificial Intelligence: Methods and Applications
- KDD Data Science for Social Good 2023
- Neural Information Processing Systems (NeurIPs)
- 5th Robot Learning Workshop: Trustworthy Robotics
- Algorithmic Fairness through the Lens of Causality and Privacy
- Causal Machine Learning for Real-World Impact
- Challenges in Deploying and Monitoring Machine Learning Systems
- Cultures of AI and AI for Culture
- Empowering Communities: A Participatory Approach to AI for Mental Health
- Federated Learning: Recent Advances and New Challenges
- Gaze meets ML
- HCAI@NeurIPS 2022, Human Centered AI
- Human Evaluation of Generative Models
- Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022
- I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical Falsification
- Learning Meaningful Representations of Life
- Machine Learning for Autonomous Driving
- Progress and Challenges in Building Trustworthy Embodied AI
- Tackling Climate Change with Machine Learning
- Trustworthy and Socially Responsible Machine Learning
- Workshop on Machine Learning Safety
- AI meets Moral Philosophy and Moral Psychology: An Interdisciplinary Dialogue about Computational Ethics
- Algorithmic Fairness through the Lens of Time
- Attributing Model Behavior at Scale (ATTRIB)
- Backdoors in Deep Learning: The Good, the Bad, and the Ugly
- Computational Sustainability: Promises and Pitfalls from Theory to Deployment
- I Can’t Believe It’s Not Better (ICBINB): Failure Modes in the Age of Foundation Models
- Socially Responsible Language Modelling Research (SoLaR)
- Regulatable ML: Towards Bridging the Gaps between Machine Learning Research and Regulations
- Workshop on Distribution Shifts: New Frontiers with Foundation Models
- XAI in Action: Past, Present, and Future Applications
- Oxford Generative AI Summit Slides
- Evaluating Generative AI Systems: the Good, the Bad, and the Hype (April 15, 2024)
- IAPP, AI Governance Global 2024, June 4-7, 2024
- Mission Control AI, Booz Allen Hamilton, and The Intellectual Forum at Jesus College, Cambridge, The 2024 Leaders in Responsible AI Summit, March 22, 2024
- OECD.AI, Building the foundations for collaboration: The OECD-African Union AI Dialogue
- NAACL 24 Tutorial: Explanations in the Era of Large Language Models
-
Documents in Legal Genres
- AI Learning Agenda - LR-142A
- An Act Addressing Innovations in Artificial Intelligence
- An Act relating to artificial intelligence; requiring disclosure of deepfakes in campaign communications; relating to cybersecurity; and relating to data privacy.
- Agenda Book for Advisory Committee on Evidence Rules – Panel on Artificial Intelligence and the Rules of Evidence
- Algorithmic Accountability Act of 2023
- Arizona, House Bill 2685
- California, Civil Rights Council - First Modifications to Proposed Employment Regulations on Automated-Decision Systems, Title 2, California Code of Regulations
- California, Consumer Privacy Act of 2018 - DIVISION 3. OBLIGATIONS [1427 - 3273.69]
- California, Senate Bill No. 53
- Cherkin et al. v. PowerSchool Holdings Inc. N.D. Cal. May 2024 – EdTech Privacy Class Action
- Popa v. Harriet Carter Gifts Inc. W.D. Pa. Mar. 2025 – Class Action on Digital Wiretapping
- Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government - 12-03)
- GDPR Complaint Filed by noyb Against OpenAI
- Germany, Bundesrat Drucksache 222/24 - Entwurf eines Gesetzes zum strafrechtlichen Schutz von Persönlichkeitsrechten vor Deepfakes
- In re Clearview AI Inc. N.D. Ill. Aug. 2022 – MDL Opinion on Amended Complaint & Retail Defendants
- Nebraska, LB1203 - Regulate artificial intelligence in media and political advertisements under the Nebraska Political Accountability and Disclosure Act
- The New York Times Company v. Microsoft Corp. OpenAI Inc. et al. December 2023 – Complaint
- The New York Times Company v. Microsoft Corporation OpenAI Inc. et al. November 2024 – Opinion & Order on Discovery Dispute
- Rhode Island, Executive Order 24-06: Artificial Intelligence and Data Centers of Excellence
- Silverman et al. v. Meta Platforms Inc. N.D. Cal. 2023 Class Action Complaint
- State of North Carolina Executive Order No. 24, Advancing Trustworthy Artificial Intelligence That Benefits All North Carolinians
- Texas draft of responsible AI bill by Capriglione
- Thaler v. Perlmutter March 2025 – Appellate Opinion on Copyright and Artificial Intelligence
- Washington State, SB 6513 - 2019-20
- United States Congress, 118th Congress, H.R.5586 - DEEPFAKES Accountability Act - 2024
- United States Congress, 118th Congress, H.R. 9720, AI Incident Reporting and Security Enhancement Act - 2024
- United States Congress, 118th Congress, S.4769 - VET Artificial Intelligence Act - 2024
- Willis v. Bank National Association as Trustee Igloo Series Trust LLC
- Illinois, Biometric Information Privacy Act
-
-
Miscellaneous Resources
-
AI Law, Policy, and Guidance Trackers
- IAPP Global AI Legislation Tracker
- IAPP US State Privacy Legislation Tracker
- George Washington University Law School's AI Litigation Database
- University of North Texas, Artificial Intelligence (AI) Policy Collection
- The Ethical AI Database
- Institute for the Future of Work, Tracking international legislation relevant to AI at work
- Legal Nodes, Global AI Regulations Tracker: Europe, Americas & Asia-Pacific Overview
- OECD.AI, National AI policies & strategies
- Raymond Sun, Global AI Regulation Tracker
- Runway Strategies, Global AI Regulation Tracker
- VidhiSharma.AI, Global AI Governance Tracker
-
AI Incident Information Sharing Resources
- AI Incident Database (Responsible AI Collaborative)
- AI Vulnerability Database (AVID)
- AIAAIC
- OECD AI Incidents Monitor
- Verica Open Incident Database (VOID)
- Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database
- AI Badness: An open catalog of generative AI badness
- AI Risk Database
- EthicalTech@GW, Deepfakes & Democracy Initiative
-
Challenges and Competitions
-
Curated Bibliographies
- Proposed Guidelines for Responsible Use of Explainable Machine Learning (presentation, bibliography)
- Proposed Guidelines for Responsible Use of Explainable Machine Learning (paper, bibliography)
- A Responsible Machine Learning Workflow (paper, bibliography) - information-2019?style=social)
- Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
- Blair Attard-Frost, INF1005H1S: Artificial Intelligence Policy Supplementary Reading List
- White & Case, AI Watch: Global regulatory tracker - United States
-
List of Lists
- AI Ethics Resources
- AI Tools and Platforms
- OECD-NIST Catalogue of AI Tools and Metrics
- OpenAI Cookbook - cookbook?style=social)
- Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance
- Tech & Ethics Curricula
- Ravit Dotan's Resources
- AI Ethics Guidelines Global Inventory
- Casey Fiesler's AI Ethics & Policy News spreadsheet
-
Critiques of AI
- Machine Learning: The High Interest Credit Card of Technical Debt
- Emergent and Predictable Memorization in Large Language Models
- Data and its (dis)contents: A survey of dataset development and use in machine learning research - 7.pdf)
- Against predictive optimization
- AI chatbots use racist stereotypes even after anti-racism training
- AI Is a Lot of Work
- AI Tools Still Permitting Political Disinfo Creation, NGO Warns
- Are Emergent Abilities of Large Language Models a Mirage?
- Artificial Hallucinations in ChatGPT: Implications in Scientific Writing
- Artificial intelligence and illusions of understanding in scientific research
- Aylin Caliskan's publications
- Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
- Evaluating Language-Model Agents on Realistic Autonomous Tasks
- Generative AI: UNESCO study reveals alarming evidence of regressive gender stereotypes
- Get Ready for the Great AI Disappointment
- Identifying and Eliminating CSAM in Generative ML Training Data and Models
- Insanely Complicated, Hopelessly Inadequate
- Lazy use of AI leads to Amazon products called “I cannot fulfill that request”
- Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs
- Low-Resource Languages Jailbreak GPT-4
- Measuring the predictability of life outcomes with a scientific mass collaboration
- Most CEOs aren’t buying the hype on generative AI benefits
- Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models
- Researchers surprised by gender stereotypes in ChatGPT
- Scalable Extraction of Training Data from (Production) Language Models
- Task Contamination: Language Models May Not Be Few-Shot Anymore
- The Cult of AI
- The Data Scientific Method vs. The Scientific Method
- Why We Must Resist AI’s Soft Mind Control
- Winner's Curse? On Pace, Progress, and Empirical Rigor
- Futurism, Disillusioned Businesses Discovering That AI Kind of Sucks
- Generative AI’s environmental costs are soaring — and mostly secret
- LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks
- Long-context LLMs Struggle with Long In-context Learning
- Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models
- The mechanisms of AI hype and its planetary and social costs
- Nepotistically Trained Generative-AI Models Collapse
- Non-discrimination Criteria for Generative Language Models
- Sustainable AI: Environmental Implications, Challenges and Opportunities
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- AI Safety Is a Narrative Problem
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- Meta’s AI chief: LLMs will never reach human-level intelligence
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Speed of AI development stretches risk assessments to breaking point
- AI already uses as much energy as a small country. It’s only the beginning.
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- FABLES: Evaluating faithfulness and content selection in book-length summarization
- Generative AI’s environmental costs are soaring — and mostly secret
- Ghost in the Cloud: Transhumanism’s simulation theology
- Internet of Bugs, Debunking Devin: "First AI Software Engineer" Upwork lie exposed! (video)
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- There Is No A.I.
- What’s in a Name? Experimental Evidence of Gender Bias in Recommendation Letters Generated by ChatGPT
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Ed Zitron's Where's Your Ed At
- Generative AI’s environmental costs are soaring — and mostly secret
- Julia Angwin, Press Pause on the Silicon Valley Hype Machine
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Which Humans?
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Ryan Allen, Explainable AI: The What’s and Why’s, Part 1: The What
- Companies like Google and OpenAI are pillaging the internet and pretending it’s progress
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- ChatGPT is bullshit
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Theory Is All You Need: AI, Human Cognition, and Decision Making
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- ChatGPT is bullshit
- Generative AI’s environmental costs are soaring — and mostly secret
- I Will Fucking Piledrive You If You Mention AI Again
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- ChatGPT is bullshit
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Quantifying Memorization Across Neural Language Models
- Re-evaluating GPT-4’s bar exam performance
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- ChatGPT is bullshit
- How AI lies, cheats, and grovels to succeed - and what we need to do about it
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- The AI Carbon Footprint and Responsibilities of AI Scientists
- The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT
- The Environmental Price of Intelligence: Evaluating the Social Cost of Carbon in Machine Learning
- Generative AI’s environmental costs are soaring — and mostly secret
- The Hidden Environmental Impact of AI
- The mechanisms of AI hype and its planetary and social costs
- Promoting Sustainability: Mitigating the Water Footprint in AI-Embedded Data Centres
- Sustainable AI: Environmental Implications, Challenges and Opportunities
- ChatGPT is bullshit
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- AI is effectively ‘useless’—and it’s created a ‘fake it till you make it’ bubble that could end in disaster, veteran market watcher warns
- Are Language Models Actually Useful for Time Series Forecasting?
- ChatGPT is bullshit
- Gen AI: Too Much Spend, Too Little Benefit?
- Meta AI Chief: Large Language Models Won't Achieve AGI
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- We still don't know what generative AI is good for
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- ChatGPT is bullshit
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- Re-evaluating GPT-4’s bar exam performance
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
- ChatGPT is bullshit
- Re-evaluating GPT-4’s bar exam performance
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- Generative AI’s environmental costs are soaring — and mostly secret
- The mechanisms of AI hype and its planetary and social costs
-
Groups and Organizations
-
-
Education Resources
-
Comprehensive Software Examples and Tutorials
- COMPAS Analysis Using Aequitas
- Explaining Quantitative Measures of Fairness (with SHAP)
- Getting a Window into your Black Box Model
- H20.ai, From GLM to GBM Part 1
- H20.ai, From GLM to GBM Part 2
- IML
- Interpreting Machine Learning Models with the iml Package
- Interpretable Machine Learning using Counterfactuals
- Machine Learning Explainability by Kaggle Learn
- Model Interpretability with DALEX
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- Interpreting Deep Learning Models for Computer Vision
- Partial Dependence Plots in R
- PiML Medium Tutorials
- PiML-Toolbox Examples - Toolbox?style=social)
- Saliency Maps for Deep Learning
- Visualizing ML Models with LIME
- Visualizing and debugging deep convolutional networks
- What does a CNN see?
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- Model Interpretation Strategies
- Hands-on Machine Learning Model Interpretation
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- The Importance of Human Interpretable Machine Learning
- Interpretable Machine Learning with Python
- The Importance of Human Interpretable Machine Learning
- Reliable-and-Trustworthy-AI-Notebooks - and-Trustworthy-AI-Notebooks?style=social)
- The Importance of Human Interpretable Machine Learning
- Explaining Quantitative Measures of Fairness with SHAP
- Getting a Window into your Black Box Model
- From GLM to GBM Part 1
- From GLM to GBM Part 2
- IML
- Interpreting Machine Learning Models with the iml Package
- Model Interpretability with DALEX
- Hands-on Machine Learning Model Interpretation
- Model Interpretation Strategies
- The Importance of Human Interpretable Machine Learning
- PiML Medium Tutorials
- Saliency Maps for Deep Learning
-
Free-ish Books
- César A. Hidalgo, Diana Orghian, Jordi Albo-Canals, Filipa de Almeida, and Natalia Martin, 2021, *How Humans Judge Machines*
- Charles Perrow, 1984, *Normal Accidents: Living with High-Risk Technologies*
- Charles Perrow, 1999, *Normal Accidents: Living with High-Risk Technologies with a New Afterword and a Postscript on the Y2K Problem*
- Deborah G. Johnson and Keith W. Miller, 2009, *Computer Ethics: Analyzing Information Technology*, Fourth Edition
- Ed Dreby and Keith Helmuth (contributors) and Judy Lumb (editor), 2009, *Fueling Our Future: A Dialogue about Technology, Ethics, Public Policy, and Remedial Action*
- George Reynolds, 2002, *Ethics in Information Technology*
- George Reynolds, 2002, *Ethics in Information Technology*, Instructor's Edition
- Kenneth Vaux (editor), 1970, *Who Shall Live? Medicine, Technology, Ethics*
- Kush R. Varshney, 2022, *Trustworthy Machine Learning: Concepts for Developing Accurate, Fair, Robust, Explainable, Transparent, Inclusive, Empowering, and Beneficial Machine Learning Systems*
- Marsha Cook Woodbury, 2003, *Computer and Information Ethics*
- M. David Ermann, Mary B. Williams, and Claudio Gutierrez, 1990, *Computers, Ethics, and Society*
- Morton E. Winston and Ralph D. Edelbach, 2000, *Society, Ethics, and Technology*, First Edition
- Morton E. Winston and Ralph D. Edelbach, 2003, *Society, Ethics, and Technology*, Second Edition
- Morton E. Winston and Ralph D. Edelbach, 2006, *Society, Ethics, and Technology*, Third Edition
- Patrick Hall and Navdeep Gill, 2019, *An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI*, Second Edition
- Patrick Hall, Navdeep Gill, and Benjamin Cox, 2021, *Responsible Machine Learning: Actionable Strategies for Mitigating Risks & Driving Adoption*
- Paula Boddington, 2017, *Towards a Code of Ethics for Artificial Intelligence*
- Przemyslaw Biecek and Tomasz Burzykowski, 2020, *Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With examples in R and Python*
- Przemyslaw Biecek, 2023, *Adversarial Model Analysis*
- Raymond E. Spier (editor), 2003, *Science and Technology Ethics*
- Richard A. Spinello, 1995, *Ethical Aspects of Information Technology*
- Richard A. Spinello, 1997, *Case Studies in Information and Computer Ethics*
- Richard A. Spinello, 2003, *Case Studies in Information Technology Ethics*, Second Edition
- Solon Barocas, Moritz Hardt, and Arvind Narayanan, 2022, *Fairness and Machine Learning: Limitations and Opportunities*
- Soraj Hongladarom and Charles Ess, 2007, *Information Technology Ethics: Cultural Perspectives*
- Stephen H. Unger, 1982, *Controlling Technology: Ethics and the Responsible Engineer*, First Edition
- Stephen H. Unger, 1994, *Controlling Technology: Ethics and the Responsible Engineer*, Second Edition
- Christoph Molnar, 2021, *Interpretable Machine Learning: A Guide for Making Black Box Models Explainable*
- christophM/interpretable-ml-book - ml-book?style=social)
- Trustworthy Machine Learning: Concepts for Developing Accurate, Fair, Robust, Explainable, Transparent, Inclusive, Empowering, and Beneficial Machine Learning Systems
- Artificial Intelligence and Fundamental Rights: The AI Act of the European Union and its implications for global technology regulation
- Computer Power and Human Reason: From Judgment to Calculation
- Regulating under Uncertainty: Governance Options for Generative AI
- The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence
- Trustworthy AI: African Perspectives
-
Glossaries and Dictionaries
- A.I. For Anyone: The A-Z of AI
- The Alan Turing Institute: Data science and AI glossary
- Appen Artificial Intelligence Glossary
- Brookings: The Brookings glossary of AI and emerging technologies
- Built In, Responsible AI Explained
- Center for Security and Emerging Technology: Glossary
- CompTIA: Artificial Intelligence (AI) Terminology: A Glossary for Beginners
- Council of Europe Artificial Intelligence Glossary
- Coursera: Artificial Intelligence (AI) Terms: A to Z Glossary
- Dataconomy: AI dictionary: Be a native speaker of Artificial Intelligence
- Dennis Mercadal, 1990, *Dictionary of Artificial Intelligence*
- G2: 70+ A to Z Artificial Intelligence Terms in Technology
- General Services Administration: AI Guide for Government: Key AI terminology
- Google Developers Machine Learning Glossary
- H2O.ai Glossary
- IAPP Glossary of Privacy Terms
- IAPP International Definitions of Artificial Intelligence
- IAPP Key Terms for AI Governance
- ISO: Information technology — Artificial intelligence — Artificial intelligence concepts and terminology
- Jerry M. Rosenberg, 1986, *Dictionary of Artificial Intelligence & Robotics*
- MakeUseOf: A Glossary of AI Jargon: 29 AI Terms You Should Know
- Moveworks: AI Terms Glossary
- National Institute of Standards and Technology (NIST), The Language of Trustworthy AI: An In-Depth Glossary of Terms
- Oliver Houdé, 2004, *Dictionary of Cognitive Science: Neuroscience, Psychology, Artificial Intelligence, Linguistics, and Philosophy*
- Otto Vollnhals, 1992, *A Multilingual Dictionary of Artificial Intelligence (English, German, French, Spanish, Italian)*
- Raoul Smith, 1989, *The Facts on File Dictionary of Artificial Intelligence*
- Raoul Smith, 1990, *Collins Dictionary of Artificial Intelligence*
- Salesforce: AI From A to Z: The Generative AI Glossary for Business Leaders
- Stanford University HAI Artificial Intelligence Definitions
- TechTarget: Artificial intelligence glossary: 60+ terms to know
- TELUS International: 50 AI terms every beginner should know
- University of New South Wales, Bill Wilson, The Machine Learning Dictionary
- Wikipedia: Glossary of artificial intelligence
- William J. Raynor, Jr, 1999, *The International Dictionary of Artificial Intelligence*, First Edition
- William J. Raynor, Jr, 2009, *International Dictionary of Artificial Intelligence*, Second Edition
- IBM: AI glossary
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- European Commission, EU-U.S. Terminology and Taxonomy for Artificial Intelligence - Second Edition
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Open Access Vocabulary
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Salesforce: AI From A to Z: The Generative AI Glossary for Business Leaders
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- European Commission, Glossary of human-centric artificial intelligence
- National Institute of Standards and Technology (NIST), NIST AI 100-2 E2023: Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- IAPP Key Terms for AI Governance
- IEEE, A Glossary for Discussion of Ethics of Autonomous and Intelligent Systems, Version 1
- ISO/IEC DIS 22989(en) Information technology — Artificial intelligence — Artificial intelligence concepts and terminology
- Siemens, Artificial Intelligence Glossary
- Towards AI, Generative AI Terminology — An Evolving Taxonomy To Get You Started
- UK Parliament, Artificial intelligence (AI) glossary
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- VAIR (Vocabulary of AI Risks)
- Artificial intelligence and illusions of understanding in scientific research (glossary on second page)
- Appen Artificial Intelligence Glossary
- Artificial intelligence and illusions of understanding in scientific research
- Artificial Intelligence Definitions
- Artificial Intelligence Glossary
- Center for Security and Emerging Technology: Glossary
- IBM AI glossary
- ISO/IEC DIS 22989 Information technology — Artificial intelligence — Artificial intelligence concepts and terminology
- Lexicon
- The Language of Trustworthy AI: An In-Depth Glossary of Terms
-
Open-ish Classes
- An Introduction to Data Ethics
- Certified Ethical Emerging Technologist
- Coursera, DeepLearning.AI, Generative AI for Everyone
- Coursera, DeepLearning.AI, Generative AI with Large Language Models
- Coursera, Google Cloud, Introduction to Generative AI
- Coursera, Vanderbilt University, Prompt Engineering for ChatGPT
- CS103F: Ethical Foundations of Computer Science
- Fairness in Machine Learning
- Fast.ai Data Ethics course
- Human-Centered Machine Learning
- Introduction to AI Ethics
- INFO 4270: Ethics and Policy in Data Science
- Machine Learning Fairness by Google
- Jay Alammar, Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)
- Introduction to Generative AI
- DeepLearning.AI
- Carnegie Mellon University, Computational Ethics for NLP
- Piotr Sapieżyński's CS 4910 - Special Topics in Computer Science: Algorithm Audits
- Transformer Models and BERT Model
- Introduction to Responsible AI
- Certified Ethical Emerging Technologist
- Google Cloud Skills Boost
- OECD.AI, Disability-Centered AI And Ethics MOOC
- Build a Large Language Model (From Scratch) - from-scratch?style=social)
- Attention Mechanism
- Create Image Captioning Models
- Encoder-Decoder Architecture
- Introduction to Image Generation
- Introduction to Large Language Models
- Introduction to Vertex AI Studio
- IBM SkillsBuild
- Awesome LLM Courses - ai/awesome-llm-courses?style=social)
- ETH Zürich ReliableAI 2022 Course Project repository - Trustworthy-AI?style=social)
- AWS Skill Builder
- Trustworthy Deep Learning
- Introduction to Responsible Machine Learning
- Certified Ethical Emerging Technologist
- Data Ethics course
- Generative AI for Educators
- Attention Mechanism
- Create Image Captioning Models
- Encoder-Decoder Architecture
- Introduction to Image Generation
- Introduction to Large Language Models
- Introduction to Vertex AI Studio
- Human-Centered Machine Learning
- INFO 4270: Ethics and Policy in Data Science
- Machine Learning Fairness by Google
-
Podcasts and Channels
-
-
AI Incidents, Critiques, and Research Resources
-
Groups and Organizations
- Algorithmic Justice League
- Partnership on AI
- Stanford University Human-Centered Artificial Intelligence
- The Alan Turing Institute
- Center for Humane Technology
- Future of Life Institute
- TheGovLab
- Center for Democracy and Technology
- AI Now Institute
- Data & Society
- Aapti Institute
- AI & Faith
- AI Ethics Lab
- AI for Good Foundation
- AI Forum New Zealand, AI Governance Working Group
- AI Hub for Sustainable Development
- AI Policy Exchange
- AI Transparency Institute
- AI Village
- Berkman Klein Center for Internet & Society at Harvard University
- Center for Advancing Safety of Machine Intelligence
- Center for Security and Emerging Technology
- Convergence Analysis
- Distributed AI Research Institute
- Global Center on AI Governance
- Indigenous Protocol and Artificial Intelligence Working Group
- Institute for Ethics and the Common Good, Notre Dame-IBM Technology Ethics Lab
- Leverhulme Centre for the Future of Intelligence
- Montreal AI Ethics Institute
- Responsible Artificial Intelligence Institute
- Ada Lovelace Institute
- Center for AI and Digital Policy
-
Critiques of AI
- Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI
- Towards Environmentally Equitable AI via Geographical Load Balancing
- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
- Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad
- Sustainable AI: AI for sustainability and the sustainability of AI
- The Leaderboard Illusion
- Medical large language models are vulnerable to data-poisoning attacks
- AI as Normal Technology
- AI Bias is Not Ideological. It's Science.
- AI can only do 5% of jobs, says MIT economist who fears tech stock crash
- AI coding assistants do not boost productivity or prevent burnout, study finds
- AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business
- AI hype, promotional culture, and affective capitalism
- Anthropomorphism in AI: hype and fallacy
- Are Emergent Abilities of Large Language Models a Mirage?
- Artificial Hype
- Artificial intelligence-powered chatbots in search engines: a cross-sectional study on the quality and risks of drug information for patients
- Artificial Intelligence: Hope for Future or Hype by Intellectuals?
- ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs
- Authoritarian by Design: AI, Big Tech, and the Architecture of Control
- Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks
- Beyond Preferences in AI Alignment
- Can We Trust AI Agents? An Experimental Study Towards Trustworthy LLM-Based Multi-Agent Systems for AI Ethics
- ChatGPT is bullshit
- Companies like Google and OpenAI are pillaging the internet and pretending it’s progress
- Data and its discontents: A survey of dataset development and use in machine learning research
- Does current AI represent a dead end?
- Evaluating Language-Model Agents on Realistic Autonomous Tasks
- Gen AI: Too Much Spend, Too Little Benefit?
- Handling the hype: Implications of AI hype for public interest tech projects
- How AI hype impacts the LGBTQ + community
- How to Tell if Something is AI-Written
- The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
- It’s Time to Stop Taking Sam Altman at His Word
- Large Language Models are Unreliable for Cyber Threat Intelligence
- Large Language Models Do Not Simulate Human Psychology
- Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models
- Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs
- LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks
- Meta AI Chief: Large Language Models Won't Achieve AGI
- MIT Technology Review, Introducing: The AI Hype Index
- The Most Dangerous Fiction: The Rhetoric and Reality of the AI Race
- On the Very Real Dangers of the Artificial Intelligence Hype Machine
- OpenAI—written evidence, House of Lords Communications and Digital Select Committee inquiry: Large language models
- Former OpenAI Researcher Says the Company Broke Copyright Law
- Open Problems in Technical AI Governance
- Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models
- Prohibiting Generative AI in any Form of Weapon Control
- Promising the future, encoding the past: AI hype and public media imagery
- Re-evaluating GPT-4’s bar exam performance
- Sam Altman’s imperial reach
- Scalable Extraction of Training Data from Production Language Models
- Talking existential risk into being: a Habermasian critical discourse perspective to AI hype
- Task Contamination: Language Models May Not Be Few-Shot Anymore
- The Fallacy of AI Functionality
- The harms of terminology: why we should reject so-called “frontier AI”
- The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’
- The Price of Emotion: Privacy, Manipulation, and Bias in Emotional AI
- There’s Nothing Magical in the Machine
- This AI Pioneer Thinks AI Is Dumber Than a Cat
- Three different types of AI hype in healthcare
- Toward Sociotechnical AI: Mapping Vulnerabilities for Machine Learning in Context
- We still don't know what generative AI is good for
- What’s in a Name? Experimental Evidence of Gender Bias in Recommendation Letters Generated by ChatGPT
- Which Humans?
- Why the AI Hype is Another Tech Bubble
- Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
- A bottle of water per email: the hidden environmental costs of using AI chatbots
- AI already uses as much energy as a small country. It’s only the beginning.
- AI, Climate, and Regulation: From Data Centers to the AI Act
- Artificial Intelligence and Environmental Impact: Moving Beyond Humanizing Vocabulary and Anthropocentrism
- Beyond AI as an environmental pharmakon: Principles for reopening the problem-space of machine learning's carbon footprint
- Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models
- The Climate and Sustainability Implications of Generative AI
- Data centre water consumption
- Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence
- Ensuring a carbon-neutral future for artificial intelligence
- Environment and sustainability development: A ChatGPT perspective
- Generative AI’s environmental costs are soaring — and mostly secret
- Green Intelligence Resource Hub
- Measuring the Environmental Impact of Delivering AI at Google Scale
- Microsoft’s Hypocrisy on AI
- Power Hungry Processing: Watts Driving the Cost of AI Deployment?
- Powering artificial intelligence: A study of AI's environmental footprint—today and tomorrow, November 2024
- The Carbon Footprint of Artificial Intelligence
- The carbon impact of artificial intelligence
- The growing energy footprint of artificial intelligence
- The Hidden Cost of AI: Carbon Footprint and Mitigation Strategies
- The Hidden Cost of AI: Unraveling the Power-Hungry Nature of Large Language Models
- The Hidden Costs of AI-driven Data Center Demand: Five Systemic Tensions
- The Hidden Environmental Impact of AI
- The mechanisms of AI hype and its planetary and social costs
- Toward Responsible AI Use: Considerations for Sustainability Impact Assessment
- Towards A Comprehensive Assessment of AI's Environmental Impact
- Towards green and sustainable artificial intelligence: quantifying the energy footprint of logistic regression and decision tree algorithms
- Tracking the carbon footprint of global generative artificial intelligence
- Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions
- We did the math on AI's energy footprint. Here's the story you haven't heard.
- Health Care Misinformation: An artificial intelligence challenge for low-resource languages
- The Serendipity of Claude AI: Case of the 13 Low-Resource National Languages of Mali
- AI Slop Might Finally Cure Our Internet Addiction
- Living the Slop Life
- Sustainable AI: AI for sustainability and the sustainability of AI
- Medical large language models are vulnerable to data-poisoning attacks
- Medical large language models are vulnerable to data-poisoning attacks
- Sustainable AI: AI for sustainability and the sustainability of AI
- Medical large language models are vulnerable to data-poisoning attacks
- Sustainable AI: AI for sustainability and the sustainability of AI
- Medical large language models are vulnerable to data-poisoning attacks
- Sustainable AI: AI for sustainability and the sustainability of AI
- Medical large language models are vulnerable to data-poisoning attacks
- Sustainable AI: AI for sustainability and the sustainability of AI
-
List of Lists
- Awesome interpretable machine learning - interpretable-machine-learning?style=social)
- Awful AI - ai?style=social)
- Awesome-ML-Model-Governance - ML-Model-Governance?style=social)
- Awesome MLOps - mlops?style=social)
- Awesome Responsible AI
- Machine Learning Interpretability Resources - resources?style=social)
- criticalML
- Machine Learning Ethics References - Learning-Ethics-References?style=social)
- XAI Resources
- awesomelistsio/Awesome AI Ethics - ai-ethics?style=social)
- MIT AI Agent Index
- Awesome AI Guidelines - artificial-intelligence-guidelines?style=social)
- A Living and Curated Collection of Explainable AI Methods
- 2024 AI Resources
- A review of 200 guidelines and recommendations for AI governance
- Awesome-explainable-AI - ntu/Awesome-explainable-AI?style=social)
- Comments Received for RFI on Artificial Intelligence Risk Management Framework - BTG
- GET Program for AI Ethics and Governance Standards
- Global Digital Policy Roundup March 2025
- LLM-Evals-Catalogue - BTG/LLM-Evals-Catalogue?style=social) | IMDA-BTG
- private-ai-resources - ai-resources?style=social)
- Ravit Dotan's Resources
- ResponsibleAI
- Ultraopxt/Awesome AI Ethics & Safety - AI-Ethics-Safety/?style=social)
- xaience
- AI Ethics Guidelines Global Inventory
- Awesome Production Machine Learning - machine-learning-operations?style=social)
-
AI Law, Policy, and Guidance Trackers
-
AI Incident Information Sharing Resources
- Atlas of AI Risks
- Brennan Center for Justice, Artificial Intelligence Legislation Tracker
- Mitre's AI Risk Database - atlas/ai-risk-database?style=social)
- Resemble.AI Deepfake Incident Database
- A comprehensive taxonomy of hallucinations in Large Language Models
- AI Ethics Issues in Real World: Evidence from AI Incident Database
- Artificial Intelligence Incidents & Ethics: A Narrative Review
- Artificial Intelligence Safety and Cybersecurity: A Timeline of AI Failures
- Deployment Corrections: An Incident Response Framework for Frontier AI Models
- Exploring Trust With the AI Incident Database
- Indexing AI Risks with Incidents, Issues, and Variants
- Good Systems, Bad Data?: Interpretations of AI Hype and Failures
- Hidden Risks: Artificial Intelligence and Hermeneutic Harm
- How Does AI Fail Us? A Typological Theorization of AI Failures
- New Noodlophile Stealer Distributes Via Fake AI Video Generation Platforms
- Omission and Commission Errors Underlying AI Failures
- Ontologies for Reasoning about Failures in AI Systems
- Planning for Natural Language Failures with the AI Playbook
- Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database
- SoK: How Artificial-Intelligence Incidents Can Jeopardize Safety and Security
- The Atlas of AI Incidents in Mobile Computing: Visualizing the Risks and Benefits of AI Gone Mobile
- Understanding and Avoiding AI Failures: A Practical Guide
- When Your AI Becomes a Target: AI Security Incidents and Best Practices
- AI Risk Database
-
Challenges and Competitions
-
Curated Bibliographies
-
Programming Languages
Categories
Sub Categories
Community Frameworks and Guidance
550
Official Policy, Frameworks, and Guidance
539
Critiques of AI
271
Open Source/Access Responsible AI Software Packages
204
Comprehensive Software Examples and Tutorials
151
Conferences and Workshops
90
Glossaries and Dictionaries
86
Open-ish Classes
48
List of Lists
36
Free-ish Books
35
Groups and Organizations
34
AI Incident Information Sharing Resources
33
Documents in Legal Genres
29
Benchmarks
29
Archived
25
Common or Useful Datasets
19
AI Law, Policy, and Guidance Trackers
17
Curated Bibliographies
7
Machine Learning Environment Management Tools
7
Challenges and Competitions
5
Podcasts and Channels
4
Personal Data Protection Tools
1
Keywords
machine-learning
55
interpretability
25
xai
22
explainable-ai
21
python
21
data-science
18
interpretable-machine-learning
17
deep-learning
15
explainable-ml
14
ai
13
fairness
12
visualization
9
artificial-intelligence
9
interpretable-ai
9
tensorflow
8
bias
8
responsible-ai
8
iml
8
llm
7
scikit-learn
7
interpretable-ml
7
natural-language-processing
7
fairness-ai
6
r
6
transparency
6
explainability
5
awesome-list
5
nlp
5
pytorch
5
shapley
5
fairness-ml
4
discrimination
4
explainable-artificial-intelligence
4
causal-inference
4
data-mining
4
xgboost
4
llm-security
4
feature-importance
4
fairness-testing
4
ml
4
chatgpt
3
openai
3
keras
3
llm-evaluation
3
privacy
3
trustworthy-ai
3
random-forest
3
security
3
h2o
3
causality
3